{"paper_id":"4b4f0e08-0669-4209-9fe4-ebcb3efb0932","body_text":"Twenty four-hour sleep, movement and sedentary activity profiles in adults living with Rheumatoid Arthritis: A cross-sectional latent class analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Twenty four-hour sleep, movement and sedentary activity profiles in adults living with Rheumatoid Arthritis: A cross-sectional latent class analysis Lynne Feehan, Hui Xie, Na Lu, Linda C Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3861599/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Rheumatoid Arthritis (RA) is an auto-immune systemic inflammatory disease, affecting more than 17 million people globally. People with RA commonly have other chronic health conditions, have a higher risk for premature mortality, often experience chronic fatigue, pain and disrupted sleep and are less physically active and more sedentary than healthy counterparts. What remains unclear is how people with RA may balance their time sleeping and participating in non-ambulatory or walking activities over 24-hours. Nor is it known how different 24-hour sleep-movement patterns may be associated with common determinants of health in people with RA. Methods We conducted a cross-sectional exploration of objectively measured 24-hour walking, non-ambulatory, and sleep activities in 203 adults with RA. We used Latent Class Analysis to identify 24-hour sleep-movement profiles and examined how different profiles were associated with sleep, sitting and walking quality and meeting published guidelines. We conducted multinomial logistic regression to identify factors associated with likelihood of belonging to individual profiles. Results We identified 4 clusters, including one cluster (26%) with more balanced 24-hour sleep, sitting and walking behaviours. The other three clusters demonstrated progressively less balanced profiles; having either too little (< 7 hrs), too much (> 8 hrs), or enough sleep (7–8 hrs) in respective combination with sitting too much (> 12 hrs), walking to little (< 3 hrs) or both when awake. Age, existing sitting and walking habits and fatigue were associated with the likelihood of belonging to different profiles. More balanced 24-hour behaviour was associated with better metrics for sleep, sitting and walking quality and greater likelihood for meeting benchmarks for daily steps, weekly MVPA and Canadian 24-hour movement guidelines. Discussion For adults living with RA, and potentially other chronic health conditions, it is important to understand the ‘whole person’ and their ‘whole day’ to define who may benefit from support to modify 24-hour sleep-movement behaviours and for tailoring healthy lifestyle messages for which behaviours to modify. Supports should be are informed by an understanding of personal or health related factors that could be acting as barriers or facilitators to behaviour change including exploring how habitually engrained existing sitting or walking behaviours may be. Trial Registrations ClinicalTrials.gov ID NCT02554474 (2015-09-16) and ClinicalTrials.gov ID NCT03404245 (2018-01-11) Rheumatoid Arthritis 24-hour sleep movement sedentary behavior Latent Class Analysis Figures Figure 1 BACKGROUND Rheumatoid Arthritis (RA) is the most common form of auto-immune systemic inflammatory joint diseases. RA can present at any age, with onset commonly occurring between the ages of 30 and 60 [ 1 ]. It is estimated that in 2020 more than 17 million people globally were living with RA, with prevalence varying globally from 50 to 200 /100,000 people, with females 2 to 3 times more likely than males to have RA [ 2 , 3 ]. RA typically presents with a rapid onset of pain, swelling and stiffness in the small joints of the hands and feet, as well as in multiple other joints. It commonly occurs on both sides of the body and does not settle over several weeks. Once diagnosed, treatment is primarily through medications with most people having to take medications for the rest of their life to reduce the number of joints involved and limit any damage to other organs such as the heart, lungs and eyes [ 4 ]. People living with RA commonly have other chronic health conditions, including cardiovascular and respiratory conditions, diabetes, and depression [ 5 , 6 ]. As such, adults living with RA have a markedly higher risk for progressive functional decline, reduced quality of life, and premature mortality [ 7 ]. People with RA often experience chronic sleep disruption, fatigue and pain all of which may contribute to, or be exacerbated by, a reduced ability to participate in higher intensity activities and for being more sedentary throughout their day [ 8 , 9 , 10 ]. They are also less physically active, more sedentary than healthy adults of the same age and sex [ 8 , 11 , 12 , 13 ]. Reducing sedentary time, increasing light activity and higher intensity activities are independently associated with a greater likelihood of improved long term health outcomes in this population, [ 14 , 15 ] similar to adults living with other chronic health condition [ 16 ]. What remains unclear is how people with RA may spend their time in sleep, sedentary activities and physical activity over 24 hours [ 17 , 18 ]. Additionally, little is know on how different combinations of objectively measured 24-hour sleep-movement patterns may be associate with meeting published evidence-based benchmarks for walking, Moderate to Vigorous Physical Activity (MVPA) or 24-hour movement guidelines. It is also unknown how variations in 24-hour sleep-movement behaviors may be associated with or common determinants of health for people with RA such as personal, socio-economic, physical / mental health and other lifestyle characteristics [ 19 , 20 , 21 ]. In a previous exploratory study, we reported distinctly different patterns of 24-hour movement behaviours in 172 adults living with osteoarthritis or inflammatory arthritis [ 22 ]. Notably, the study identified a subgroup of individuals who achieved a balance of sleep, moving and sitting throughout their day [ 22 ]. Our aim was to build on this previous work and focus on examining patterns of 24-hour sleep-movement behaviour patterns in a larger cohort living with RA. The objectives were to identify unique clusters of objectively measured sleep, sitting or standing still (non-ambulatory activities), and walking behaviours over 24-hours and describe differences across the clusters for variations in time spent in different sleep and awake movement behaviours. We also wanted to examine differences across the clusters for variations in sleep, sitting and walking quality and for meeting published evidence-based benchmarks for adults, including: 1) daily step volume [6000 to 8000 steps / day] [ 23 ], 2) weekly MVPA (> 150 minutes / week) [ 24 ], and 3) Canadian 24-hour movement guidelines [sleep: 7 to 8 hours, sitting: <10 hours [ 25 ] and MVPA: 25 + minutes] [ 26 ]. Finally, we wanted to explore the associations between personal demographic and socio-economic characteristics, baseline physical and mental health, and walking and sitting habits and the likelihood of individuals belonging to specific 24-hour sleep-movement behaviour profiles [ 27 , 28 ]. METHODS Design We conducted a cross-sectional analysis of baseline data in a cohort of 203 participants from two randomized clinical trials. Baseline assessments were completed between 2017 and 2022. Participants The sample included adults living with RA who consented to participate in one of two randomized clinical trials examining the efficacy of community-based, physical therapist lead, technology-enabled, physical activity counselling interventions [Onlin e Physical Activity Monitoring in Inflammatory Arthritis (OPAM-IA) OR or On-demand Program to EmpoweR Active Self-management (OPERAS)] [ 29 , 30 ]. Baseline data were collected in both studies prior to randomization. Participants were recruited to either study from primarily urban rheumatology clinics in British Columbia (BC), Canada, through arthritis patient group networks, and from postings on social media and Arthritis Research Canada’s website. Individuals were eligible if they: 1) had a rheumatologist confirmed diagnosis of RA [ 31 ], 2) had no surgery or injury to any joints in the previous 6 months, 3) had an email address and access to a computer or mobile device, 4) were able to participate in physical activities without health professional supervision, and 5) were able to speak and understand English. [ 29 , 30 ]. 24-hour Sleep-Activity Measurement 24-hour sleep-activity was measured by research grade SenseWear Mini™ activity monitors (BodyMedia, Inc., Pittsburgh, PA). Sensewear monitors are multi-sensor devices that integrate personal demographic, physiologic and tri-axial accelerometry data. Sensewear monitors provide reliable and valid estimates of activity in people living with arthritis if worn for 4 or more days [ 32 , 32 , 34 , 35 ]. These devices have an excellent ability to differentiate between sedentary and light intensity activities (Positive Predictive Value: 0.81) [ 36 ]. They have also demonstrated high wake / sleep agreement (80%) and high sensitivity for sleep estimation (89%) compared to polysomnography and provide equivalent measures for time in bed compared to sleep diaries in free-living conditions [ 37 , 38 ]. Sensewear monitors turn off when not in contact with skin, providing accurate estimates of time off-body. Participants wore the monitors for 1-week on the upper arm over the triceps on the non-dominant arm and only removed them for showering or water-based activities. Sleep-Activity Data Processing Downloaded data from the devices were processed using Sensewear professional software (v8.1.0.22) with the minute-by-minute data exported and processed further using MATLAB software (R2016a, The MathWorks, Inc., Natick, Massachusetts, United States). Data included the average value of the first four to six days, with at least 20 hours of wear [ 32 , 32 , 34 , 35 ]. The outcome variables included time (minutes) / day: 1) lying down sleeping, 2) lying down awake (resting), 3) in non-ambulatory activity (likely sitting, possibly standing still), 4) in intermittent walking activities, 5) in purposeful walking activities, or 6) with the sensor off-body (unknown activity, likely showering / bathing). We used a 50 steps / minute cut-point to define intermittent verses purposeful walking [ 39 ]. Each minute could only be categorized into one of the six activity categories with the constraint that the total number of minutes across all six sleep-activity categories had to add up to 1440 minutes (24 hours) From these data, we also calculated total time in bed (lying down sleeping + resting) and total walking time (intermittent + purposeful walking). In addition, we extracted time spent in bouted sitting (20 + minutes of uninterrupted non-ambulatory minutes, at < 1.5 Metabolic Equivalents (METs), time spent in MVPA (4 + METs) and total daily steps. From these we calculated selected quality metrics for sleeping, sitting and walking behaviours. These included sleep efficiency (percentage of time sleeping while lying in bed), prolonged sitting behaviour (percentage of sitting time spent in bouts of 20 or more minutes), awake movement balance (percentage of time walking when awake), and walking quality (percentage of walking time spent in higher cadence ambulation). Self-Reported Demographic Characteristics and Baseline Health Outcomes Participants provided information on their demographic (age, sex, height, weight) and socio-economic characteristics (usual occupation, highest education, annual household income, marital status). Pain was measured with the short form McGill Pain Questionnaire (SF-MPQ), using 15-pain related words that can be rated from 0 (none) to 3 (severe). Scores vary from 0 to 45, with scores below 15 indicating no to mildly discomforting levels of pain [ 40 ]. Fatigue was measured using the Fatigue Severity Scale (FSS), a nine-item questionnaire about fatigue and how it affects daily activities, rated on 7-point Likert scale (strongly disagree to strongly agree). A score of 4 or higher is considered clinically relevant fatigue. [ 41 , 42 ]. Depression was measured using the Patient Health Questionnaire-9 (PHQ-9), a nine-item questionnaire about common symptoms of depression, rated on a 4-point frequency scale (1-not at all, 4-almost daily). A score of 5 or less indicates no or minimal depression [ 43 , 44 ]. Participants rated their strength of habit for sitting during leisure time at home, sitting during usual occupational activities, and walking outside for 10 minutes or more using the Self-Reported Habit Index (SRHI). The SRHI is a 12-item scale, rating specific behaviours done within a defined setting or context, rated on a 7-point Likert scale (strongly disagree to strongly agree), with higher scores indicating a stronger habitual behaviour that is done frequently, automatically, and without thinking about it [ 45 , 46 ]. STATISTICAL ANALYSES All statistical analyses were conducted using SAS v9.4 software (SAS Institute Inc., North Carolina, USA. There were no missing data from any individual for any sleep-activity measures or self-reported demographic characteristics or baseline health outcomes examined in this study. Latent Class Analyses: Best Fit We conducted a Latent Class Analysis (LCA) using time (minutes) spent in each of six sleep-awake activity categories across 1440 minutes (24-hours) [ 47 ]. We used Akaike’s and Bayesian Information Criterion (AIC/BIC) model comparison analyses to identify the best fit for number of clusters [ 48 ]. 24-Hour Sleep-Movement Behaviours and Quality We used descriptive statistics [mean and standard deviation (SD)] to compare differences across clusters and relative to the whole cohort for: 1) time spent in each of the six 24-hour sleep-activity categories, 2) time spent in bed, prolonged sitting, and walking at any cadence, 3) total daily steps and MVPA, and 4) sleeping, sitting and walking quality metrics. Meeting Evidence-Based Activity Benchmarks. Using these descriptive comparisons we identified the likelihood for individuals in the cohort as a whole and within each cluster for meeting published evidence-based benchmarks for adults, including: 1) daily step volume [6000 to 8000 steps / day] [ 23 ], 2) weekly MVPA volume (> 150 minutes / week) [ 24 ], and 3) Canadian 24-hour sleep movement guidelines [sleep: 7 to 8 hours, sitting: <10 hours [ 25 ] and MVPA: 25 + minutes] [ 26 ]. Baseline Characteristic Comparisons We compared differences across the identified clusters, relative to the whole cohort, for personal demographic, socio-economic, physical / mental health, sitting and walking habit strength, study participation, and covid activity restrictions characteristics. We used mean and SD for continuous variables and number and percentages for categorical variables for descriptive comparisons. In addition, we calculated mean percent difference (% Diff) for each baseline characteristics within each cluster relative to the cohort mean. We also explored for statistically significant differences in baseline characteristic across clusters using Analyses of Variance for continuous variables and Chi-square tests for categorical variables, where statistical significance indicated that amongst the 4 cluster values, that at least two of them are significantly different from each other. These analyses were for descriptive purposes only, given the multiple statistical comparisons. Association: Baseline Characteristics and Cluster Allocation We conducted multinomial logistic regression with backward elimination to identify factors associated with likelihood of individuals belonging to a specific cluster profile, with the most inactive cluster being the reference cluster. We included all personal demographic, socio-economic, physical / mental health, and sitting / walking habit strength factors, as well as, study participation (OPAM vs. OPERAS) and covid related activity restrictions into the model. The effect of factors remaining in the final model are reported as Odds Ratio (OR) point estimates with the Wald 95% confidence intervals (95% CI) relative to a reference cluster (OR: 1.0). RESULTS Cohort Characteristics The cohort included 203 individuals, who were predominantly female (92%), older aged (Age Mean: 56, SD: 13 years) and with a mean BMI of 28 (SD: 7 kg/m 2 ). Fifty-eight percent (n = 118) of the cohort were recruited for the OPERAS study, and 39% (n = 80) were assessed when varying levels of mandated COVID-19 activity limitations were in place in BC. Less than half of the cohort were employed (45%), had a university degree (46%) or had an annual household income greater than $ 80K (41%). Whereas, 65% of the cohort had a marital spouse or partner. Of the cohort, 60% reported having no or minimal depression (PHQ-9 score ≤ 5). On average the cohort also reported having mild pain (SF-MPQ Mean: 12, SD: 9) and clinically relevant levels of fatigue (FSS Mean: 4.7, SD: 1.3). In addition, the cohort reported having neither strong or weak habitual leisure time sitting (SRHI: mean: 4.7, SD: 1.3), usual occupational sitting (SRHI: Mean: 4.5, SD: 1.7) or walking outside (SRHI: Mean 4.3, SD: 1.7) behaviours [Table 1 ]. Table 1 Baseline Characteristics: Whole Cohort vs Clusters High Sit / Low Walk (Inactive) High Sleep / Low Walk Low Sleep / High Sit Most Balanced Cluster Differences: P-value Whole Cohort Number [n (%)] 30 (15%) 63 (31%) 57 (28%) 53 (26%) n/a 203 (100%) Personal Demographics Age Years [Mean (SD)] 61.8 (12.7) 52.3 (12.6) 60.7 (12.5) 52.7 (11.9) < .001 56.2 (13.0) Sex = Female [n (%)] 25 (83.3%) 58 (92.1%) 52 (91.2%) 51 (96.2%) 0.24 186 (91.6%) BMI - kg / m2 [Mean (SD)] 28.4 (6.6) 28.2 (8.8) 26.6 (5.9) 26.9 (5.8) 0.52 27.5 (7.0) Socio-Economic Characteristics Employed = Yes [n (%)] 9 (30%) 30 (47.6%) 27 (47.4%) 26 (49.1%) 0.34 92 (45.3%) Spouse / Common Law Partner = Yes [n (%)] 17 (56.7%) 38 (60.3%) 38 (66.7%) 39 (73.6%) 0.35 132 (65.0%) Annual Household Income [n (%)] 0.64 $ 80 K or less 17 (56.6%) 29 (46.0%) 20 (35.1%) 23 (43.4%) 89 (43.9%) Over $ 80k 9 (30.0%) 24 (38.1%) 27 (47.4%) 23 (43.4%) 83 (40.9%) Unknown 4 (13.3%) 10 (15.9%) 10 (17.5%) 7 (13.2%) 31 (15.3%) University Degree = Yes [n (%)] 12 (40.0%) 29 (46.0%) 24 (42.1%) 29 (54.7%) 0.49 94 (46.3%) Physical / Mental Health Depression (PHQ-9): Mild to Severe (Score ≥ 5) [n (%)] 17 (56.7%) 45 (71.4%) 28 (49.1%) 32 (60.4%) 0.22 122 (60.1%) Fatigue (FSS) [1 to 7, Higher = More Fatigue [Mean (SD)] 4.6 (1.2) 5.1 (1.3) 4.3 (1.3) 4.8 (1.3) < .001 4.7 (1.3) Pain (SF-MPQ) [0 to 45, Higher = More Pain.[Mean (SD)] 12.4 (8.9) 14.4 (10.3) 9.5 (7.6) 11.5 (8.9) 0.03 12.0 (9.2) Habit Strength Sitting at home, leisure time (SRHI) [1 to 7, Higher = Stronger Habit, Mean (SD)] 5.2 (1.0) 4.8 (1.3) 4.6 (1.3) 4.2 (1.3) 0.01 4.7 (1.3) Sitting during usual occupational activity (SRHI) [1 to 7, Higher = Stronger Habit, Mean (SD))] 4.8 (1.5) 4.7 (1.7) 4.9 (1.4) 3.8 (1.7) < .001 4.5 (1.6) Walking, outside, > 10 minutes (SRHI) [1 to 7, Higher = Stronger Habit ,Mean (SD)] 3.7 (1.6) 4.0 (1.8) 4.6 (1.5) 4.7 (1.7) 0.01 4.3 (1.7) External (Temporal) Factors Covid Activity Restrictions = Yes [n (%)] 10 (33.3%) 29 (46%) 21 (36.8%) 20 (37.7%) 0.61 80 (39.4%) Study = OPERAS [n (%)] 15 (50%) 40 (63.5%) 33 (57.9%) 30 (56.6%) 0.66 118 (58.1%) Bold = Statistically Significant. BMI - Body Mass Index. SF-MPQ: Short Form-McGill Pain Questionnaire. FSS: Fatigue Severity Scale. PHQ-9: Patient Health Questionnaire-9. SRHI: Self Reported Habit Index. OPERAS: On-demand Program to EmpoweR Active Self-management. Insert Table 1 here On average, participants spent 453 (SD: 79) mins / day sleeping, 671 (SD: 101) mins / day in non-ambulatory activity, 175 (SD 68) mins / day in intermittent walking, and 28 (SD: 20) mins / day in purposeful walking. They accumulated on average 5,650 steps a day (SD: 2,774) and 17 mins / day in MVPA (SD: 22). Individuals spent 84% of their time in bed sleeping (i.e. sleep efficiency) and 77% of their awake time in sitting or standing still activities. Approximately 48% of their non-ambulatory time was spent in prolonged sitting. Only 23% of their awake time included ambulatory activities (i.e. movement balance), with 14% of their total walking time spent in higher cadence walking (i.e. walking quality). Other than meeting the recommended daily sleeping recommendations within the 24-hour movement guidelines, the cohort did not meet the 24-hour movement guidelines for time spent sitting or in higher intensity. People in the cohort also did not meet evidence-based benchmarks for recommended daily steps or weekly MVPA [Table 2 ]. Table 2 Across-Cluster vs Whole Cohort Comparisons: 24-hour sleep movement behaviours, quality and meeting published benchmarks High Sit / Low Wal (Inactive) High Sleep / Low Walk Low Sleep / High Sit Most Balanced Whole Cohort Number [n (%) ] 30 (15%) 63 (31%) 57 (28%) 53 (26%) 203 (100%) 24 - Hour Sleep / Movement - Time Breakdown Off body (mins) [Mean (SD)] 20.7 (11.2) 24.9 (13.6) 29.7 (15.8) 28.5 (25.7) 26.6 (18.0) Time in Bed - Lying Down (mins) [Mean (SD)] 525.9 (48.4) 621.7 (82.5) 463.8 (55.2) 530.2 (80.4) 539.3 (93.3) Sleep (mins) [Mean (SD)] 440.1 (55.8) 516.8 (66.1) 404.7 (54.9) 435.4 (76.7) 452.7 (78.6) Rest (mins) [Mean (SD)] 85.8 (47.7) 104.9 (23.6) 59.0 (18.0) 94.9 (49.2) 86.6 (47.5) Awake - Non-Ambulatory (Mins) [Mean (SD)] 746.6 (49.7) 626.8 (64.9) 746.8 (61.2) 581.9 (77.7) 670.9 (101.1) Awake - Bouted (Sedentary) Sitting (mins) [Mean (SD)] 447.1 (156.2) 312.3 (100.0) 392.5 (119.1) 209.6 (90.7) 332.4 (144.3) Awake - Non-Bouted Sitting (mins) [Mean (SD)] 299.5 (139.6) 314.4 (84.9) 354.3 (90.5) 372.3 (75.0) 338.5 (97.5) Awake - Ambulatory (mins) [Mean (SD)] 116.8 (40.8) 166.7 (43.1) 199.8 (43.2) 299.4 (57.0) 203.3 (78.2) Intermittent Ambulatory (mins) [Mean (SD)] 106.2 (36.6) 143.7 (36.7) 169.7 (44.7) 256.4 (50.5) 174.9 (67.6) Purposeful Ambulatory (mins) [Mean (SD)] 10.6 (10.8) 23.0 (15.1) 30.0 (15.9) 42.9 (23.1) 28.3 (20.2) 24-Hour Sleep / Movement Quality Sleep Efficiency (%) [Mean (SD)] 83.8% (8.6%) 83.5% (6.8%) 87.1% (4.0%) 82.1% (9.2%) 84.2% (7.4%) Prolonged Sitting Behaviour (%) [Mean (SD)] 61.1% (17.9%) 49.4% (14.1%) 52.0% (13.5%) 35.3% (12.9%) 48.2% (16.5%) Awake Movement Balance (%) [Mean (SD)] 13.0% (4.4%) 20.9% (4.9%) 21.1% (4.5%) 34.0% (6.0%) 23.2% (8.6%) Walking Quality (%) [Mean (SD)] 8.8% (6.9%) 13.0% (7.8%) 15.9% (10.0%) 14.1% (6.8%) 13.5% (8.4%) Daily: Steps and MVPA Volume * Steps (steps / day) [Mean (SD)] 2723.0 (1720.8) 4553.5 (1840.7) 5473.9 (1694.9) 8507.5 (2524.4) 5649.6 (2773.8) ** MVPA (mins / day) [Mean (SD)] 6.8 (12.8) 15.2 (19.4) 18.3 (21.3) 25.4 (27.7) 17.5 (22.3) Meeting Published Benchmarks / Guidelines Walking Volume (6000 to 8000 / day) [Yes / No] No No No Yes (exceeds) No MVPA guidelines (> 150 mins / week of higher intensity activity) [Yes / No] No No No Yes No 24-hour sleep-movement guidelines (7 to 8 hours sleep, < 10 hours sitting, MVPA: 25 + minutes / day) [meeting 0,1,2, or 3 elements] 1 (sleep) 0 0 3 (sleep, sit, MVPA) 1 (sleep) Bold = The six 24-hour sleep-activity variables included in the Latency Class Analyses (Total: 1440 minutes / day). * Steps includes steps accumulated through any type of ambulation at any intensity. ** MVPA (Moderate to Vigorous Activity) includes time spent in any type of higher intensity activity. Strongly correlated with purposeful walking (correlation coefficient 0.51, p < 0.001) Insert Table 2 here Latent Class Analyses We conducted a LCA exploring patterns of objectively measured for time over 24-hours for non-wear, lying down sleeping or resting, and awake non-ambulatory and walking (intermittent or purposeful) activities. We identified 4 unique clusters as the best fit using AIC/BIC model comparison analyses. Across-Cluster Comparison: 24-hour Sleep and Awake Activity Behaviours See Table 2 for details of time spent in different sleep and awake activities over 24-hours and likelihood for meeting activity guidelines comparisons across clusters and relative to the whole cohort. Overall, there were no notable differences in time of non-wear across clusters, with a mean off-body time / day varying from 21 to 30 minutes. We identified one cluster of 53 individuals (26%) showing an overall more balanced 24-hour sleep-movement profile ( i.e. Most Balanced Cluster ). Individuals in this cluster averaged 435 (SD: 77 mins) minutes of sleep and 582 (SD: 78) minutes in non-ambulatory activities. They also averaged 256 (SD:51) minutes in intermittent walking and 43 (SD:23) minutes in purposeful walking, accumulating on average 8508 (SD: 2524) steps a day. Those in this cluster also averaged 25 (SD:28) minutes a day of higher intensity activity. As such, individuals in this most balanced clusters were likely to meet all of the sleeping, sitting and MVPA elements within the 24-hour sleep-movement guidelines. In addition, individuals in this cluster exceeded published benchmarks for recommended daily steps and weekly MVPA [Table 2 , Fig. 1 ]. Insert Fig. 1 here We identified a second smaller cluster of 30 individuals (15%) that although averaging 440 (SD:56) minutes of sleep a day, demonstrated a more inactive life style when they were awake ( i.e. High Sit / Low Walk Cluster ). Individuals in this most inactive cluster spent on average 747 (SD:50) minutes a day in non-ambulatory activities. In addition, they only averaged 106 (SD:37) minutes a day in intermittent walking and 11(SD:11) minutes a day in purposeful walking, accumulating on average only 2723 (SD:1721) steps a day. As well, they averaged only 7 (SD:13) minutes a day in higher intensity activities. As such, members in this most inactive cluster only met the sleep recommendations of the within the 24-hour Movement Guidelines and did not meet the daily steps or weekly MVPA benchmarks [Table 2 , Fig. 1 ]. We also identified two additional clusters, characterized by either too few (< 7 hours) or too many (> 8 hours) hours sleeping [ 49 ]. The cluster with too much sleep (i.e. High Sleep / Low Walk Cluster, n = 63, 31% ) averaged 517 (SD:66) minutes of sleep and 627 (SD:65) minutes in non-ambulatory activities. Individuals in this cluster averaged 144, (SD:37) minutes a day in intermittent walking and 23, (SD:15) minutes a day in purposeful walking, accumulating an average of 4554 (SD: 1841) daily steps. Those in this cluster also averaged 15 minutes a day (SD: 19) in higher intensity activities. Therefore, individuals in this high sleep cluster did not meet the 24-hour movement guidelines or the daily step or weekly MVPA benchmarks [Table 2 , Fig. 1 ]. Participants in the low sleeping cluster (i.e. Low Sleep / High Sit Cluster, n = 57, 28%) averaged 517 (SD: 66) minutes sleeping and 747 (SD:6 ) minutes in non-ambulatory activities. Individuals in this cluster walked intermittently on average 169.7, (SD: 44.7) minutes a day and purposefully for 30.0 (SD: 15.9) minutes a day, accumulating a mean of 5474 steps each day (SD:1695). In addition, they spent on average of 18 minutes a day (SD: 21) in higher intensity activities. As such, individuals in this cluster also did not meet any of the 24-hour Movement Guidelines, or the daily step or the weekly MVPA benchmarks [Table 2 , Fig. 1 ]. Across-Cluster Comparison: Sleep, Sitting and Walking Quality. See Table 2 for further details of sleep, sitting and walking quality. All clusters demonstrated an average sleep efficiency of greater than 80% [ 50 ]. However, only the low sleep cluster had a sleep efficiency over 85% (Mean: 87%, SD: 4%) [ 50 ]. Indicating that although individuals in the low sleep cluster spent less time in bed, they were the most efficient sleepers. Progressing from the most balanced through to the most inactive clusters, the time spent in prolonged sitting behaviours progressively increased. With the most balanced cluster spending an average 35% (SD:13%) of their sitting time in prolonged sitting activities compared to the most inactive cluster spending on average 61% (SD: 18%) of there sitting time in prolonged sitting activities. Indicating that not only were people in the most inactive cluster sitting for a greater percentage of time in their day, they also spent a greater percentage of sitting time in prolonged sitting activities. Conversely, and also progressing from the most balanced through to the most inactive clusters, the time spent walking when awake (i.e. movement balance) progressively decreased. With the most balanced cluster spending on average 34% (SD:6%) of their time when awake walking compared to the most inactive cluster spending on average 13% (SD:4%) of their awake time walking. Notably, all but the most inactive cluster had similar walking quality metrics, with the average walking quality in the more balanced, low sleeper and high sleeper clusters varying from 14–16%. Whereas, those in the most inactive cluster spent on average only 9% (SD:7%) of their walking time in higher cadence walking activities. Indicating that not only were people in the most inactive cluster spending a lower percentage of their day walking around, they also spent a smaller percentage of their walking time in higher cadence walking activities. Across-Cluster Comparison: Baseline Characteristics See Table 1 for details of baseline characteristics across clusters and relative to the whole cohort, and Table 3 for details of percentage differences across clusters, relative to the whole cohort. Age was the only personal demographic characteristic that was significantly different across the clusters. Relative to the whole cohort, those in the most balanced and high sleeper clusters were younger (% Diff: -6.2 and − 6.9% younger), compared to those in low sleeper and most inactive clusters being older (% Diff: 8% and 10% older). None of the socio-economic characteristics were significantly different across the clusters, although relative to whole cohort, those in the most balanced cohort were more likely to have a marital spouse or partner, have a university education and have an annual household income greater than $ 80, with an opposite trend for these same socio-economic characteristics in the most inactive cluster [Table 3 ]. Table 3 Percent Difference Cluster vs Whole Cohort: Selected Baseline Characteristics Most Inactive -Whole High Sleeper -Whole Low Sleeper -Whole Most Balanced -Whole Personal Demographics Age (Years) 10.0% -6.9% 8.0% -6.2% Sex (% Female) -9.8% 0.0% -1.1% 4.3% BMI (kg / m2) 3.3% 2.5% -3.3% -2.2% Socio-Economic Characteristics Employed -33.8% 5.1% 4.6% 8.4% Spouse / Partner -12.8% -7.2% 2.6% 13.2% Annual Household Income > $ 80K -26.7% -6.8% 15.9% 6.1% University Degree -13.6% -0.6% -9.1% 18.1% Physical / Mental Health Depression -5.7% 18.8% -18.3% 0.5% Fatigue -2.1% 8.5% -8.5% 2.1% Pain 3.3% 20.0% -20.8% -4.2% Habit Strength Sitting – Home Leisure 10.6% 2.1% -2.1% -10.6% Sitting – Usual Occupation 6.7% 4.4% 8.9% -15.6% Walking Outside -14.0% -7.0% 7.0% 9.3% External Factors Covid Activity Restriction -15.5% 16.8% -6.6% -4.3% OPERAS Participant -13.9% 9.3% -0.3% -2.6% BMI: Body Mass Index. OPERAS: On-demand Program to EmpoweR Active Self-management Insert Table 3 here Pain and fatigue scores were also significantly different across clusters with these differences being most apparent when comparing the high and low sleeping clusters. Relative to the whole cohort, those in the low sleeping cluster reported lower levels of fatigue (% Diff: -8.5% less fatigue) and pain (% Diff: -20.8% less pain). Where as, those in the high sleeping cluster reported higher levels of, fatigue (% Diff: 8.5% more fatigue) and pain (% Diff: 20.0% more pain) relative to the whole cohort [Table 3 ]. Leisure time sitting, usual occupational sitting and outside walking habit scores were also significantly different across the clusters. Notably, those in the most balanced cluster reported lower leisure time (% Diff: -10.6% weaker habit) and usual occupational sitting (% Diff: -15.6% weaker habit) habits and higher walking outside (% Diff: 9.3% stronger habit) habit scores relative to the whole cohort. Which is in contrast to those in the most inactive cluster reporting higher leisure time (% Diff: 10.6% stronger habit) and usual occupational sitting (% Diff: 6.7% stronger habit) habits and lower walking outside (% Diff: -14.0% weaker) habits relative to the whole cohort [Table 3 ] Finally, there was no significant difference across clusters for the proportion of those in either the OPERAS or OPAM-IA study or for those assess during covid activity restrictions [Tables 2 and 3 ]. Baseline Characteristics and Likelihood of Cluster Allocation See Table 4 for details of the likelihood (Odd ratio) of different cluster allocation relative to a reference cluster for baseline characteristics remaining in the model following backwards, stepwise, multivariate regression analyses. Analysis highlighted that determinants of different patterns of 24-hour sleep-movement behaviours was multifactorial. Individuals were more likely to be allocated to the more balanced cluster, relative to the most inactive cluster, if they were a younger age (OR: 0.94, 95% CI: 0.90–0.98), had stronger walking outside habits (OR:1.44, 95% CI: 1.05–1.97) and weaker leisure time sitting habits (OR:0.62, 95% CI: 0.39–0.98 ). In addition, relative to the low sleep / high sit cluster, weaker usual occupational sitting habits was also associated with a greater likelihood of being in the more balanced cluster (OR: 0.61, 95% CI: 0.45–0.81). Stronger walking outside habits was also associated with a greater likelihood of being in the low sleep / high sit cluster, relative to the most inactive cluster (OR:1.36, 95% CI: 1.01–1.84). While, younger age (OR:0.94, 95% CI: 0.90–0.98) and greater fatigue (OR:1.59, 95% CI: 1.07–2.36) were associated with greater likelihood of being allocated to the high sleep / low walk cluster relative to the most inactive cluster. Multivariate regression analyses also found that sex, BMI, socio-economic factors, pain, depression, the study individuals volunteered for or the potential impacts of covid activity restrictions were not associated with cluster allocation. Table 4 Likelihood of cluster allocation relative to reference cluster. Odd Ratio (95% Confidence Interval) Factors Included in Model Factors Remaining in Model Most Balanced Low Sleep / High Sit High sleep / Low Walk High Sit / Low Walk (Reference) Personal Demographic Characteristics : Age (Years), Sex (F vs M), BMI (kg/m2) Age 0.94 (0.90–0.98) 0.99 (0.95–1.04) 0.94 (0.90–0.98) 1.0 Socio-Economic Factors : Spouse / Partner (yes / no), University Education (yes / no), Annual Household Income (+ / - $ 80K, unknown), Employed (yes / no) None n/a n/a n/a n/a Physical / Mental Health Indicators : Pain (score), Fatigue (score), Depression - Mild to Severe (yes / no ) Fatigue 1.47 (0.98–2.21) 0.97 (0.67–1.40) 1.59 (1.07–2.36) 1.0 Sitting Habits : Home Leisure, Usual Occupational (score) Sitting - Home Leisure 0.62 (0.39–0.98) 0.65 (0.40–1.01) 0.72 (0.45–1.14) 1.0 *Sitting - Usual Occupational 0.61 (0.45–0.81) 1.0 0.76 (0.57, 1.02) 0.81 (0.58, 1.13) Sitting - Usual Occupational 0.74 (0.54–1.03) 1.24 (0.88–1.73) 0.94 (0.68–1.29) 1.0 Walking Habits : Walking Outside > 10 minutes (score) Walking - Outside 1.44 (1.05–1.97) 1.36 (1.01–1.84) 1.15 (0.85–1.55) 1.0 Study Participation: OPERAS (yes / no) None n/a n/a n/a n/a Covid Activity Restriction: (yes / no) None n/a n/a n/a n/a Bold = Statistically Significant * Most Balanced vs Low sleep / High Sit cluster as reference Insert Table 4 here DISCUSSION This study explored objectively measured sleep and awake behaviours in a cohort of adults living with RA, to identify unique patterns of 24-hour sleep and movement behaviours, and their association with common personal, socio-economic, physical, mental and existing sitting and walking habits. This study also presents how different patterns of 24-hour behaviours were associated with variations in sleep, sitting and walking quality, and the likelihood of meeting evidence-based benchmarks for steps and MVPA and 24-hour movement guidelines. We found that the cohort as a whole was getting acceptable sleep duration and quality. However, they spent more than three quarters of their awake time sitting, with almost half of their sitting time accumulated doing prolonged sitting activities. In addition, not only were people in the cohort spending less than a quarter of their day walking they were also only spending a small portion of their walking time in higher cadence walking activities. As such, other than getting acceptable sleep, the cohort as a whole did not meet benchmarks for walking, higher intensity activity or balanced 24-hour movement behaviours. These findings are consistent with previously published studies showing that on average adults with RA are generally more sedentary and less active than similar aged people, and commonly do not meet recommended daily steps or weekly MVPA recommendations [ 51 ]. When we used LCA to explore this further we found four distinctive patterns for how the adults living with RA were spending time sleeping and in awake movement activities throughout their day. One cluster, representing almost a quarter of the cohort, presented with more balanced 24-hour sleep-movement behaviour profile. Whereas, those in the other three clusters demonstrated progressively less balanced behaviour profiles; having either too little (< 7 hours), too much (> 8 hours), or enough sleep (7–8 hours) in respective combination with sitting too much (> 12 hours), walking to little (< 3 hours) or both when they were awake. We also found that having more balanced 24-hour sleep-movement behaviours was associated with better metrics for sleep, sitting and walking quality, and a greater likelihood of meeting evidence-based benchmarks for daily steps, MVPA and Canadian 24-hour movement guidelines. Together these findings suggests that many people living with RA can have a more balanced 24-hour sleep-movement lifestyle which may in turn be associated with better physical and mental health. Future research should explore how more balanced and various combinations of less balanced 24-hour sleep and awake movement behaviours may be associated with improved health outcomes in people living with RA, and other chronic health conditions. Our findings also highlight that the importance of tailoring healthy lifestyle messages based on how individuals are actually spending their time sleeping, sitting and walking throughout their day. For some, the message would be “you are doing well, keep it up”. In others, who have lower levels of sleep and spend many hours sitting the focus would be about finding opportunities to replace sitting at home or during their usual occupational activities with more time in bed sleeping (“sleep more / sit less”). Whereas, those with too much sleep in combination with low levels of walking outside their home the focus would be more around finding opportunities to replace time in bed with outdoor walking activities (“walk more / sleep less”). Alternately, for others that have acceptable sleep but are inactive when awake, then the attention would be about finding ways to replace sitting with walking activities (“walk more / sit less”) [ 18 , 28 ], A distinctive finding is the association between existing sitting and walking habits and the likelihood of having a more or less balanced 24-hour sleep-activity profile while living with RA. Habitual behaviours are actions, or series of actions, that occur with limited conscious thought, often in response to contextual or environmental cues [ 52 , 53 ]. The relationship between existing habits and future behaviours is complex, as habits can have both a moderating and mediating effect on future behaviours [ 54 , 55 ]. Pre-existing habits can be a predictor of future behaviours, independent of the intention to do a behaviour (i.e. habit as a mediator of future behaviour) [ 56 ]. As such, strong existing habitual behaviours are likely to increase the likelihood of future similar behaviours in similar contexts. However, strong existing habitual behaviours can also moderate the potential effect of the intention or desire of changing behaviours in similar contexts [ 57 ]. This speaks to the old adage that “old habits are hard to break”, which in turn may explain in part why strategies supporting someone to be more physically active do not necessarily change their existing sitting behaviours [ 58 ]. These finding support further explorations of the influence of strong or weak sitting or walking habits on activity-related health behavior change interventions in future investigations [ 53 ]. Our findings also highlight that differences in age, and physical or mental health may also be associated with having more or less balanced 24-hour sleep and movement profile. Notably age is not a modifiable factor, however, our findings do support the value of understanding not only the potential influence of existing habitual behaviours, but also the importance of managing co-existing factors like fatigue and pain when supporting a persons’ capacity, opportunity or motivation to modify their daily sleeping or movement behaviours [ 51 , 59 ] This study has limitations. Our findings would have limited generalizability to adults living with other chronic health conditions, or person’s living with RA that are not inclined to volunteer for research studies or those with RA that did not meet the eligibility criteria for either study. This study is an exploratory, cross sectional study so any associations between differences in 24-hour sleep-activity behaviours and baseline characteristics cannot be defined in terms of the directionality of this relationship. It is possible that living with RA affects a person’s 24-hour sleep and movement activity behaviours and / or that variations in 24-hour sleep-activity behaviours impact the physical, mental or other health outcomes in people living with RA [ 60 ]. Future studies should explore these potential relationships using longitudinal observational or experimental study designs. Another limitation is our use of research grade activity tracker to objectively measure 24-hour sleep and movement patterns, as these devices are expensive and not readily available. However, in clinical or usual life situations, where accurate minute by minute data for research purposes is not required, it is reasonable to consider using more affordable, accessible and acceptable consumer wearable activity trackers [ 61 , 62 ]. Consumer wearable devices can provide reasonable objective estimates of patterns of time spent in different activities over 24-hours to help guide and inform strategies to help a person to monitor and modify their 24-hour sleep and movement behaviours [ 63 ]. CONCLUSION For adults living with RA, and potentially other chronic health conditions, it is important to understand the ‘whole person’ and their ‘whole day’ to help define who may benefit from support with modifying their 24-hour sleep-movement behaviours. Findings also highlight the importance of tailoring healthy lifestyle messages based on how individuals are actually spending their time sleeping, sitting and walking throughout their day. Ideally, the planning and implementation of supports to modify behaviours should be guided by objective measures of sleep and awake activities and adopt a shared-decision making approach to ensure that personal preferences and priorities are considered [ 21 , 51 , 59 , 64 ]. In addition, supports should be are informed by an understanding of potentially modifiable personal or health related factors that could be acting as barriers or facilitators to behaviour change [ 65 , 66 , 67 ]. Including, exploring how habitually engrained existing sitting or walking behaviours may be, so that positive habits can be reinforced and strategies can be defined to help people identify and modify less positive habitual behaviours. Abbreviations RA: Rheumatoid Arthritis MVPA: Moderate to Vigorous Physical Activity OPAM-IA: Online Physical Activity Monitoring in Inflammatory Arthritis OPERAS: On-demand Program to EmpoweR Active Self-management METs: Metabolic Equivalents SF-MPQ: Short Form-McGill Pain Questionnaire FSS: Fatigue Severity Scale PHQ-9: Patient Health Questionnaire-9 SRHI: Self Reported Habit Index LCA: Latent Class Analysis AIC / BIC: Akaike’s and Bayesian Information Criterion SD: Standard Deviation % Diff: Percent Difference OR: Odds Ratio 95% CI: 95% Confidence Intervals Declarations Ethics approval and consent to participate. Both studies were carried out in compliance with the Helsinki declaration for conducting research with humans and received ethical approval from the University of British Columbia, Vancouver, Canada (OPAM-IA study: H15-01843, OPERAS study: H17-03424). Participants provided written informed consent which including permission to use their data for research purposes. Consent for publication Not applicable. Availability of data and materials The data are available from the corresponding author on reasonable request. Competing interests None of the authors have any direct or indirect financial or non-financial competing interests to declare. Funding This study was supported by two Arthritis Society Canada Strategic Operating Grants (Funding Reference Numbers: SOG-16-391; SOG-14-110). The funder did not have any input in the design or conduct of this study. Authors' contributions LF conceived the study idea. LCL is the lead author and person holding data stewardship for the baseline data collected from the two clinical trials from which these secondary data analyses were conducted. All authors (LF, HX, NL, LCL) were involved in the design of the study, the analyses plan and interpretation of the results. NL conducted the analyses. All authors were involved in manuscript preparation and editing. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Mar, 2024 Reviews received at journal 07 Mar, 2024 Reviews received at journal 05 Feb, 2024 Reviewers agreed at journal 24 Jan, 2024 Reviewers invited by journal 24 Jan, 2024 Editor assigned by journal 24 Jan, 2024 Submission checks completed at journal 16 Jan, 2024 First submitted to journal 13 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-3861599\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":267338407,\"identity\":\"5f1c3b48-addd-417a-b977-85ff01c1b082\",\"order_by\":0,\"name\":\"Lynne Feehan\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACPgglx8AgAaQ+ADEbOwEtbBDKGKyFcQZIhJkULcw8IDZBLRK5xx58YDCQ45dufvbY5tc2eT5mBsYPH3PwaclLN5zBYGAsOeeYuXFu323DNmYGZsmZ2/BpyTGT5mH4k7jhRoKZdG7PbUagFjZmXkJa/jAY1O+/kf5N2rLntj1xWhgYDBIMwIwftxMJa+F5YybZY2BgOONGTplkb8Pt5DZmxma8fuFnzzGT+FFhIM8/I32bxI8/t23ntzcf/PARjxYGgQQgYQDlMLaByQY86kHWHEDm/cGveBSMglEwCkYmAAAhiETP+Vay9wAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"University of British Columbia\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Lynne\",\"middleName\":\"\",\"lastName\":\"Feehan\",\"suffix\":\"\"},{\"id\":267338408,\"identity\":\"4c06719a-04c1-47f5-bdd3-a43b475c831f\",\"order_by\":1,\"name\":\"Hui Xie\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Simon Fraser University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hui\",\"middleName\":\"\",\"lastName\":\"Xie\",\"suffix\":\"\"},{\"id\":267338409,\"identity\":\"5fd70350-9c63-418f-ab6a-de436ef9fbb9\",\"order_by\":2,\"name\":\"Na Lu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Arthritis Research Centre of Canada\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Na\",\"middleName\":\"\",\"lastName\":\"Lu\",\"suffix\":\"\"},{\"id\":267338410,\"identity\":\"ed090edb-1a48-4bce-a9de-2721f65cd374\",\"order_by\":3,\"name\":\"Linda C Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of British Columbia\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Linda\",\"middleName\":\"C\",\"lastName\":\"Li\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-01-14 00:44:06\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3861599/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3861599/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":49776799,\"identity\":\"df8b8951-b5d0-47c2-9794-4eda29f84a20\",\"added_by\":\"auto\",\"created_at\":\"2024-01-17 21:13:46\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":63831,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAcross-cluster comparisons: Average time spent in each sleep-awake activity over\\u003c/p\\u003e\\n\\u003cp\\u003e24-hours (1440 minutes).\\u003c/p\\u003e\\n\\u003cp\\u003eThe images below the figure represent from left to right: 1) Off-body time (likely showering / bathing), 2) Lying down sleeping, 3) Lying down awake (resting), 4) Non-ambulatory activities (likely sitting or standing still), 5) Intermittent walking activities, and 6) Purposeful walking activities over 24-hours. Time in each sleep or activity category is identified by minutes / day (Y axis) with some additional markers embedded in the figure to identify key cut-points for sleep (\\u0026lt;7 and \\u0026gt; 8 hours), sitting (\\u0026gt;10 and \\u0026gt;12 hours), intermittent walking (\\u0026lt;3 hours) and purposeful walking (\\u0026lt; 25 minutes) .\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FeehanetalFigure1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3861599/v1/fb103bbd86e930227166a7a0.jpg\"},{\"id\":49777297,\"identity\":\"fb1b1a86-347b-40d8-a2d6-150fc173cba5\",\"added_by\":\"auto\",\"created_at\":\"2024-01-17 21:21:47\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":718922,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3861599/v1/10382a51-ea4d-45b0-88b8-3f73c065b7b1.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Twenty four-hour sleep, movement and sedentary activity profiles in adults living with Rheumatoid Arthritis: A cross-sectional latent class analysis\",\"fulltext\":[{\"header\":\"BACKGROUND\",\"content\":\" \\u003cp\\u003eRheumatoid Arthritis (RA) is the most common form of auto-immune systemic inflammatory joint diseases. RA can present at any age, with onset commonly occurring between the ages of 30 and 60 [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. It is estimated that in 2020 more than 17\\u0026nbsp;million people globally were living with RA, with prevalence varying globally from 50 to 200 /100,000 people, with females 2 to 3 times more likely than males to have RA [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. RA typically presents with a rapid onset of pain, swelling and stiffness in the small joints of the hands and feet, as well as in multiple other joints. It commonly occurs on both sides of the body and does not settle over several weeks. Once diagnosed, treatment is primarily through medications with most people having to take medications for the rest of their life to reduce the number of joints involved and limit any damage to other organs such as the heart, lungs and eyes [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003ePeople living with RA commonly have other chronic health conditions, including cardiovascular and respiratory conditions, diabetes, and depression [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. As such, adults living with RA have a markedly higher risk for progressive functional decline, reduced quality of life, and premature mortality [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. People with RA often experience chronic sleep disruption, fatigue and pain all of which may contribute to, or be exacerbated by, a reduced ability to participate in higher intensity activities and for being more sedentary throughout their day [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. They are also less physically active, more sedentary than healthy adults of the same age and sex [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Reducing sedentary time, increasing light activity and higher intensity activities are independently associated with a greater likelihood of improved long term health outcomes in this population, [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e] similar to adults living with other chronic health condition [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. What remains unclear is how people with RA may spend their time in sleep, sedentary activities and physical activity over 24 hours [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Additionally, little is know on how different combinations of objectively measured 24-hour sleep-movement patterns may be associate with meeting published evidence-based benchmarks for walking, Moderate to Vigorous Physical Activity (MVPA) or 24-hour movement guidelines. It is also unknown how variations in 24-hour sleep-movement behaviors may be associated with or common determinants of health for people with RA such as personal, socio-economic, physical / mental health and other lifestyle characteristics [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn a previous exploratory study, we reported distinctly different patterns of 24-hour movement behaviours in 172 adults living with osteoarthritis or inflammatory arthritis [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Notably, the study identified a subgroup of individuals who achieved a balance of sleep, moving and sitting throughout their day [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Our aim was to build on this previous work and focus on examining patterns of 24-hour sleep-movement behaviour patterns in a larger cohort living with RA. The objectives were to identify unique clusters of objectively measured sleep, sitting or standing still (non-ambulatory activities), and walking behaviours over 24-hours and describe differences across the clusters for variations in time spent in different sleep and awake movement behaviours. We also wanted to examine differences across the clusters for variations in sleep, sitting and walking quality and for meeting published evidence-based benchmarks for adults, including: 1) daily step volume [6000 to 8000 steps / day] [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e], 2) weekly MVPA (\\u0026gt;\\u0026thinsp;150 minutes / week) [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e], and 3) Canadian 24-hour movement guidelines [sleep: 7 to 8 hours, sitting: \\u0026lt;10 hours [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e] and MVPA: 25\\u0026thinsp;+\\u0026thinsp;minutes] [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Finally, we wanted to explore the associations between personal demographic and socio-economic characteristics, baseline physical and mental health, and walking and sitting habits and the likelihood of individuals belonging to specific 24-hour sleep-movement behaviour profiles [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e].\\u003c/p\\u003e\"},{\"header\":\"METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDesign\\u003c/h2\\u003e \\u003cp\\u003eWe conducted a cross-sectional analysis of baseline data in a cohort of 203 participants from two randomized clinical trials. Baseline assessments were completed between 2017 and 2022.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eParticipants\\u003c/h2\\u003e \\u003cp\\u003eThe sample included adults living with RA who consented to participate in one of two randomized clinical trials examining the efficacy of community-based, physical therapist lead, technology-enabled, physical activity counselling interventions [Onlin e Physical Activity Monitoring in Inflammatory Arthritis (OPAM-IA) OR or On-demand Program to EmpoweR Active Self-management (OPERAS)] [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Baseline data were collected in both studies prior to randomization. Participants were recruited to either study from primarily urban rheumatology clinics in British Columbia (BC), Canada, through arthritis patient group networks, and from postings on social media and Arthritis Research Canada\\u0026rsquo;s website. Individuals were eligible if they: 1) had a rheumatologist confirmed diagnosis of RA [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e], 2) had no surgery or injury to any joints in the previous 6 months, 3) had an email address and access to a computer or mobile device, 4) were able to participate in physical activities without health professional supervision, and 5) were able to speak and understand English. [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e24-hour Sleep-Activity Measurement\\u003c/h2\\u003e \\u003cp\\u003e24-hour sleep-activity was measured by research grade SenseWear Mini\\u0026trade; activity monitors (BodyMedia, Inc., Pittsburgh, PA). Sensewear monitors are multi-sensor devices that integrate personal demographic, physiologic and tri-axial accelerometry data. Sensewear monitors provide reliable and valid estimates of activity in people living with arthritis if worn for 4 or more days [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. These devices have an excellent ability to differentiate between sedentary and light intensity activities (Positive Predictive Value: 0.81) [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]. They have also demonstrated high wake / sleep agreement (80%) and high sensitivity for sleep estimation (89%) compared to polysomnography and provide equivalent measures for time in bed compared to sleep diaries in free-living conditions [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Sensewear monitors turn off when not in contact with skin, providing accurate estimates of time off-body. Participants wore the monitors for 1-week on the upper arm over the triceps on the non-dominant arm and only removed them for showering or water-based activities.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSleep-Activity Data Processing\\u003c/h2\\u003e \\u003cp\\u003eDownloaded data from the devices were processed using Sensewear professional software (v8.1.0.22) with the minute-by-minute data exported and processed further using MATLAB software (R2016a, The MathWorks, Inc., Natick, Massachusetts, United States). Data included the average value of the first four to six days, with at least 20 hours of wear [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. The outcome variables included time (minutes) / day: 1) lying down sleeping, 2) lying down awake (resting), 3) in non-ambulatory activity (likely sitting, possibly standing still), 4) in intermittent walking activities, 5) in purposeful walking activities, or 6) with the sensor off-body (unknown activity, likely showering / bathing). We used a 50 steps / minute cut-point to define intermittent verses purposeful walking [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]. Each minute could only be categorized into one of the six activity categories with the constraint that the total number of minutes across all six sleep-activity categories had to add up to 1440 minutes (24 hours)\\u003c/p\\u003e \\u003cp\\u003eFrom these data, we also calculated total time in bed (lying down sleeping\\u0026thinsp;+\\u0026thinsp;resting) and total walking time (intermittent\\u0026thinsp;+\\u0026thinsp;purposeful walking). In addition, we extracted time spent in bouted sitting (20\\u0026thinsp;+\\u0026thinsp;minutes of uninterrupted non-ambulatory minutes, at \\u0026lt;\\u0026thinsp;1.5 Metabolic Equivalents (METs), time spent in MVPA (4\\u0026thinsp;+\\u0026thinsp;METs) and total daily steps. From these we calculated selected quality metrics for sleeping, sitting and walking behaviours. These included sleep efficiency (percentage of time sleeping while lying in bed), prolonged sitting behaviour (percentage of sitting time spent in bouts of 20 or more minutes), awake movement balance (percentage of time walking when awake), and walking quality (percentage of walking time spent in higher cadence ambulation).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eSelf-Reported Demographic Characteristics and Baseline Health Outcomes\\u003c/h2\\u003e \\u003cp\\u003eParticipants provided information on their demographic (age, sex, height, weight) and socio-economic characteristics (usual occupation, highest education, annual household income, marital status). Pain was measured with the short form McGill Pain Questionnaire (SF-MPQ), using 15-pain related words that can be rated from 0 (none) to 3 (severe). Scores vary from 0 to 45, with scores below 15 indicating no to mildly discomforting levels of pain [\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. Fatigue was measured using the Fatigue Severity Scale (FSS), a nine-item questionnaire about fatigue and how it affects daily activities, rated on 7-point Likert scale (strongly disagree to strongly agree). A score of 4 or higher is considered clinically relevant fatigue. [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. Depression was measured using the Patient Health Questionnaire-9 (PHQ-9), a nine-item questionnaire about common symptoms of depression, rated on a 4-point frequency scale (1-not at all, 4-almost daily). A score of 5 or less indicates no or minimal depression [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. Participants rated their strength of habit for sitting during leisure time at home, sitting during usual occupational activities, and walking outside for 10 minutes or more using the Self-Reported Habit Index (SRHI). The SRHI is a 12-item scale, rating specific behaviours done within a defined setting or context, rated on a 7-point Likert scale (strongly disagree to strongly agree), with higher scores indicating a stronger habitual behaviour that is done frequently, automatically, and without thinking about it [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eSTATISTICAL ANALYSES\\u003c/h2\\u003e \\u003cp\\u003eAll statistical analyses were conducted using SAS v9.4 software (SAS Institute Inc., North Carolina, USA. There were no missing data from any individual for any sleep-activity measures or self-reported demographic characteristics or baseline health outcomes examined in this study.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eLatent Class Analyses: Best Fit\\u003c/h2\\u003e \\u003cp\\u003eWe conducted a Latent Class Analysis (LCA) using time (minutes) spent in each of six sleep-awake activity categories across 1440 minutes (24-hours) [\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]. We used Akaike\\u0026rsquo;s and Bayesian Information Criterion (AIC/BIC) model comparison analyses to identify the best fit for number of clusters [\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e24-Hour Sleep-Movement Behaviours and Quality\\u003c/h2\\u003e \\u003cp\\u003eWe used descriptive statistics [mean and standard deviation (SD)] to compare differences across clusters and relative to the whole cohort for: 1) time spent in each of the six 24-hour sleep-activity categories, 2) time spent in bed, prolonged sitting, and walking at any cadence, 3) total daily steps and MVPA, and 4) sleeping, sitting and walking quality metrics.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eMeeting Evidence-Based Activity Benchmarks.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eUsing these descriptive comparisons we identified the likelihood for individuals in the cohort as a whole and within each cluster for meeting published evidence-based benchmarks for adults, including: 1) daily step volume [6000 to 8000 steps / day] [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e], 2) weekly MVPA volume (\\u0026gt;\\u0026thinsp;150 minutes / week) [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e], and 3) Canadian 24-hour sleep movement guidelines [sleep: 7 to 8 hours, sitting: \\u0026lt;10 hours [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e] and MVPA: 25\\u0026thinsp;+\\u0026thinsp;minutes] [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBaseline Characteristic Comparisons\\u003c/h2\\u003e \\u003cp\\u003eWe compared differences across the identified clusters, relative to the whole cohort, for personal demographic, socio-economic, physical / mental health, sitting and walking habit strength, study participation, and covid activity restrictions characteristics. We used mean and SD for continuous variables and number and percentages for categorical variables for descriptive comparisons. In addition, we calculated mean percent difference (% Diff) for each baseline characteristics within each cluster relative to the cohort mean. We also explored for statistically significant differences in baseline characteristic across clusters using Analyses of Variance for continuous variables and Chi-square tests for categorical variables, where statistical significance indicated that amongst the 4 cluster values, that at least two of them are significantly different from each other. These analyses were for descriptive purposes only, given the multiple statistical comparisons.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssociation: Baseline Characteristics and Cluster Allocation\\u003c/h2\\u003e \\u003cp\\u003eWe conducted multinomial logistic regression with backward elimination to identify factors associated with likelihood of individuals belonging to a specific cluster profile, with the most inactive cluster being the reference cluster. We included all personal demographic, socio-economic, physical / mental health, and sitting / walking habit strength factors, as well as, study participation (OPAM vs. OPERAS) and covid related activity restrictions into the model. The effect of factors remaining in the final model are reported as Odds Ratio (OR) point estimates with the Wald 95% confidence intervals (95% CI) relative to a reference cluster (OR: 1.0).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCohort Characteristics\\u003c/h2\\u003e \\u003cp\\u003eThe cohort included 203 individuals, who were predominantly female (92%), older aged (Age Mean: 56, SD: 13 years) and with a mean BMI of 28 (SD: 7 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e). Fifty-eight percent (n\\u0026thinsp;=\\u0026thinsp;118) of the cohort were recruited for the OPERAS study, and 39% (n\\u0026thinsp;=\\u0026thinsp;80) were assessed when varying levels of mandated COVID-19 activity limitations were in place in BC. Less than half of the cohort were employed (45%), had a university degree (46%) or had an annual household income greater than \\u003cspan\\u003e$\\u003c/span\\u003e80K (41%). Whereas, 65% of the cohort had a marital spouse or partner. Of the cohort, 60% reported having no or minimal depression (PHQ-9 score\\u0026thinsp;\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e\\u0026le;\\u003c/span\\u003e\\u0026thinsp;5). On average the cohort also reported having mild pain (SF-MPQ Mean: 12, SD: 9) and clinically relevant levels of fatigue (FSS Mean: 4.7, SD: 1.3). In addition, the cohort reported having neither strong or weak habitual leisure time sitting (SRHI: mean: 4.7, SD: 1.3), usual occupational sitting (SRHI: Mean: 4.5, SD: 1.7) or walking outside (SRHI: Mean 4.3, SD: 1.7) behaviours [Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBaseline Characteristics: Whole Cohort vs Clusters\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHigh Sit / Low Walk (Inactive)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHigh Sleep / Low Walk\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLow Sleep / High Sit\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMost Balanced\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eCluster Differences: P-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eWhole Cohort\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNumber [n (%)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e30 (15%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e63 (31%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e57 (28%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e53 (26%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e203 (100%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ePersonal Demographics\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge Years [Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e61.8 (12.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52.3 (12.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60.7 (12.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e52.7 (11.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e56.2 (13.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSex\\u0026thinsp;=\\u0026thinsp;Female [n (%)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e25 (83.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e58 (92.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e52 (91.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e51 (96.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e186 (91.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI - kg / m2 [Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e28.4 (6.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28.2 (8.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e26.6 (5.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e26.9 (5.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e27.5 (7.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eSocio-Economic Characteristics\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEmployed\\u0026thinsp;=\\u0026thinsp;Yes [n (%)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9 (30%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30 (47.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e27 (47.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e26 (49.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e92 (45.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSpouse / Common Law Partner\\u0026thinsp;=\\u0026thinsp;Yes [n (%)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17 (56.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38 (60.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e38 (66.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e39 (73.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e132 (65.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnnual Household Income [n (%)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cspan\\u003e$\\u003c/span\\u003e80 K or less\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17 (56.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e29 (46.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20 (35.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e23 (43.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e89 (43.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOver \\u003cspan\\u003e$\\u003c/span\\u003e80k\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9 (30.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24 (38.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e27 (47.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e23 (43.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e83 (40.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUnknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4 (13.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10 (15.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10 (17.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7 (13.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e31 (15.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUniversity Degree\\u0026thinsp;=\\u0026thinsp;Yes [n (%)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12 (40.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e29 (46.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e24 (42.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e29 (54.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e94 (46.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ePhysical / Mental Health\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDepression (PHQ-9): Mild to Severe (Score\\u0026thinsp;\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e\\u0026ge;\\u003c/span\\u003e\\u0026thinsp;5) [n (%)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17 (56.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e45 (71.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28 (49.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e32 (60.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e122 (60.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFatigue (FSS) [1 to 7, Higher\\u0026thinsp;=\\u0026thinsp;More Fatigue [Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.6 (1.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.1 (1.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.3 (1.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.8 (1.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e4.7 (1.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePain (SF-MPQ) [0 to 45, Higher\\u0026thinsp;=\\u0026thinsp;More Pain.[Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12.4 (8.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14.4 (10.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9.5 (7.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e11.5 (8.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.03\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e12.0 (9.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eHabit Strength\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSitting at home, leisure time (SRHI) [1 to 7, Higher\\u0026thinsp;=\\u0026thinsp;Stronger Habit, Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.2 (1.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.8 (1.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.6 (1.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.2 (1.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.01\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e4.7 (1.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSitting during usual occupational activity (SRHI) [1 to 7, Higher\\u0026thinsp;=\\u0026thinsp;Stronger Habit, Mean (SD))]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.8 (1.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.7 (1.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.9 (1.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.8 (1.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e4.5 (1.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWalking, outside, \\u0026gt; 10 minutes (SRHI) [1 to 7, Higher\\u0026thinsp;=\\u0026thinsp;Stronger Habit ,Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.7 (1.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.0 (1.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.6 (1.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.7 (1.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.01\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e4.3 (1.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eExternal (Temporal) Factors\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCovid Activity Restrictions\\u0026thinsp;=\\u0026thinsp;Yes [n (%)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10 (33.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e29 (46%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21 (36.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e20 (37.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e80 (39.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStudy\\u0026thinsp;=\\u0026thinsp;OPERAS [n (%)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15 (50%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e40 (63.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33 (57.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30 (56.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.66\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e118 (58.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBold\\u003c/b\\u003e\\u0026thinsp;=\\u0026thinsp;Statistically Significant. BMI - Body Mass Index. SF-MPQ: Short Form-McGill Pain Questionnaire. FSS: Fatigue Severity Scale. PHQ-9: Patient Health Questionnaire-9. SRHI: Self Reported Habit Index. OPERAS: On-demand Program to EmpoweR Active Self-management.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eInsert\\u003c/em\\u003e Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e \\u003cem\\u003ehere\\u003c/em\\u003e\\u003c/p\\u003e \\u003cp\\u003eOn average, participants spent 453 (SD: 79) mins / day sleeping, 671 (SD: 101) mins / day in non-ambulatory activity, 175 (SD 68) mins / day in intermittent walking, and 28 (SD: 20) mins / day in purposeful walking. They accumulated on average 5,650 steps a day (SD: 2,774) and 17 mins / day in MVPA (SD: 22). Individuals spent 84% of their time in bed sleeping (i.e. sleep efficiency) and 77% of their awake time in sitting or standing still activities. Approximately 48% of their non-ambulatory time was spent in prolonged sitting. Only 23% of their awake time included ambulatory activities (i.e. movement balance), with 14% of their total walking time spent in higher cadence walking (i.e. walking quality). Other than meeting the recommended daily sleeping recommendations within the 24-hour movement guidelines, the cohort did not meet the 24-hour movement guidelines for time spent sitting or in higher intensity. People in the cohort also did not meet evidence-based benchmarks for recommended daily steps or weekly MVPA [Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAcross-Cluster vs Whole Cohort Comparisons: 24-hour sleep movement behaviours, quality and meeting published benchmarks\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHigh Sit / Low Wal (Inactive)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHigh Sleep / Low Walk\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLow Sleep / High Sit\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMost Balanced\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eWhole Cohort\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNumber [n (%) ]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e30 (15%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e63 (31%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e57 (28%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e53 (26%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e203 (100%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e24 - Hour Sleep / Movement - Time Breakdown\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eOff body (mins) [Mean (SD)]\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e20.7 (11.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.9 (13.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e29.7 (15.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e28.5 (25.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e26.6 (18.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTime in Bed - Lying Down (mins) [Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e525.9 (48.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e621.7 (82.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e463.8 (55.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e530.2 (80.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e539.3 (93.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSleep (mins) [Mean (SD)]\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e440.1 (55.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e516.8 (66.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e404.7 (54.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e435.4 (76.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e452.7 (78.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRest (mins) [Mean (SD)]\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e85.8 (47.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e104.9 (23.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e59.0 (18.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e94.9 (49.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e86.6 (47.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAwake - Non-Ambulatory (Mins) [Mean (SD)]\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e746.6 (49.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e626.8 (64.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e746.8 (61.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e581.9 (77.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e670.9 (101.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAwake - Bouted (Sedentary) Sitting (mins) [Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e447.1 (156.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e312.3 (100.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e392.5 (119.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e209.6 (90.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e332.4 (144.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAwake - Non-Bouted Sitting (mins) [Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e299.5 (139.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e314.4 (84.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e354.3 (90.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e372.3 (75.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e338.5 (97.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAwake - Ambulatory (mins) [Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e116.8 (40.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e166.7 (43.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e199.8 (43.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e299.4 (57.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e203.3 (78.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eIntermittent Ambulatory (mins) [Mean (SD)]\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e106.2 (36.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e143.7 (36.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e169.7 (44.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e256.4 (50.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e174.9 (67.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePurposeful Ambulatory (mins) [Mean (SD)]\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10.6 (10.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23.0 (15.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30.0 (15.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e42.9 (23.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e28.3 (20.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e24-Hour Sleep / Movement Quality\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSleep Efficiency (%) [Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e83.8% (8.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e83.5% (6.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e87.1% (4.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e82.1% (9.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e84.2% (7.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eProlonged Sitting Behaviour (%) [Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e61.1% (17.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e49.4% (14.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e52.0% (13.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e35.3% (12.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e48.2% (16.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAwake Movement Balance (%) [Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13.0% (4.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20.9% (4.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21.1% (4.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e34.0% (6.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e23.2% (8.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWalking Quality (%) [Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8.8% (6.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.0% (7.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15.9% (10.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14.1% (6.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e13.5% (8.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eDaily: Steps and MVPA Volume\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e* Steps (steps / day) [Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2723.0 (1720.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4553.5 (1840.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5473.9 (1694.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8507.5 (2524.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e5649.6 (2773.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e** MVPA (mins / day) [Mean (SD)]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.8 (12.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15.2 (19.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18.3 (21.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e25.4 (27.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e17.5 (22.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMeeting Published Benchmarks / Guidelines\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWalking Volume (6000 to 8000 / day) [Yes / No]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eYes (exceeds)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMVPA guidelines (\\u0026gt;\\u0026thinsp;150 mins / week of higher intensity activity) [Yes / No]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e24-hour sleep-movement guidelines (7 to 8 hours sleep, \\u0026lt; 10 hours sitting, MVPA: 25\\u0026thinsp;+\\u0026thinsp;minutes / day) [meeting 0,1,2, or 3 elements]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1 (sleep)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3 (sleep, sit, MVPA)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1 (sleep)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBold\\u003c/b\\u003e\\u0026thinsp;=\\u0026thinsp;The six 24-hour sleep-activity variables included in the Latency Class Analyses (Total: 1440 minutes / day). * Steps includes steps accumulated through any type of ambulation at any intensity. ** MVPA (Moderate to Vigorous Activity) includes time spent in any type of higher intensity activity. Strongly correlated with purposeful walking (correlation coefficient 0.51, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eInsert\\u003c/em\\u003e Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e \\u003cem\\u003ehere\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eLatent Class Analyses\\u003c/h2\\u003e \\u003cp\\u003eWe conducted a LCA exploring patterns of objectively measured for time over 24-hours for non-wear, lying down sleeping or resting, and awake non-ambulatory and walking (intermittent or purposeful) activities. We identified 4 unique clusters as the best fit using AIC/BIC model comparison analyses.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAcross-Cluster Comparison: 24-hour Sleep and Awake Activity Behaviours\\u003c/h2\\u003e \\u003cp\\u003eSee Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e for details of time spent in different sleep and awake activities over 24-hours and likelihood for meeting activity guidelines comparisons across clusters and relative to the whole cohort. Overall, there were no notable differences in time of non-wear across clusters, with a mean off-body time / day varying from 21 to 30 minutes.\\u003c/p\\u003e \\u003cp\\u003eWe identified one cluster of 53 individuals (26%) showing an overall more balanced 24-hour sleep-movement profile (\\u003cem\\u003ei.e. Most Balanced Cluster\\u003c/em\\u003e). Individuals in this cluster averaged 435 (SD: 77 mins) minutes of sleep and 582 (SD: 78) minutes in non-ambulatory activities. They also averaged 256 (SD:51) minutes in intermittent walking and 43 (SD:23) minutes in purposeful walking, accumulating on average 8508 (SD: 2524) steps a day. Those in this cluster also averaged 25 (SD:28) minutes a day of higher intensity activity. As such, individuals in this most balanced clusters were likely to meet all of the sleeping, sitting and MVPA elements within the 24-hour sleep-movement guidelines. In addition, individuals in this cluster exceeded published benchmarks for recommended daily steps and weekly MVPA [Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eInsert\\u003c/em\\u003e Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e \\u003cem\\u003ehere\\u003c/em\\u003e\\u003c/p\\u003e \\u003cp\\u003eWe identified a second smaller cluster of 30 individuals (15%) that although averaging 440 (SD:56) minutes of sleep a day, demonstrated a more inactive life style when they were awake (\\u003cem\\u003ei.e. High Sit / Low Walk Cluster\\u003c/em\\u003e). Individuals in this most inactive cluster spent on average 747 (SD:50) minutes a day in non-ambulatory activities. In addition, they only averaged 106 (SD:37) minutes a day in intermittent walking and 11(SD:11) minutes a day in purposeful walking, accumulating on average only 2723 (SD:1721) steps a day. As well, they averaged only 7 (SD:13) minutes a day in higher intensity activities. As such, members in this most inactive cluster only met the sleep recommendations of the within the 24-hour Movement Guidelines and did not meet the daily steps or weekly MVPA benchmarks [Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eWe also identified two additional clusters, characterized by either too few (\\u0026lt;\\u0026thinsp;7 hours) or too many (\\u0026gt;\\u0026thinsp;8 hours) hours sleeping [\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. The cluster with too much sleep \\u003cem\\u003e(i.e. High Sleep / Low Walk Cluster, n\\u0026thinsp;=\\u0026thinsp;63, 31% )\\u003c/em\\u003e averaged 517 (SD:66) minutes of sleep and 627 (SD:65) minutes in non-ambulatory activities. Individuals in this cluster averaged 144, (SD:37) minutes a day in intermittent walking and 23, (SD:15) minutes a day in purposeful walking, accumulating an average of 4554 (SD: 1841) daily steps. Those in this cluster also averaged 15 minutes a day (SD: 19) in higher intensity activities. Therefore, individuals in this high sleep cluster did not meet the 24-hour movement guidelines or the daily step or weekly MVPA benchmarks [Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eParticipants in the low sleeping cluster \\u003cem\\u003e(i.e. Low Sleep / High Sit Cluster, n\\u0026thinsp;=\\u0026thinsp;57, 28%)\\u003c/em\\u003e averaged 517 (SD: 66) minutes sleeping and 747 (SD:6 ) minutes in non-ambulatory activities. Individuals in this cluster walked intermittently on average 169.7, (SD: 44.7) minutes a day and purposefully for 30.0 (SD: 15.9) minutes a day, accumulating a mean of 5474 steps each day (SD:1695). In addition, they spent on average of 18 minutes a day (SD: 21) in higher intensity activities. As such, individuals in this cluster also did not meet any of the 24-hour Movement Guidelines, or the daily step or the weekly MVPA benchmarks [Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eAcross-Cluster Comparison: Sleep, Sitting and Walking Quality.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eSee Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e for further details of sleep, sitting and walking quality. All clusters demonstrated an average sleep efficiency of greater than 80% [\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]. However, only the low sleep cluster had a sleep efficiency over 85% (Mean: 87%, SD: 4%) [\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]. Indicating that although individuals in the low sleep cluster spent less time in bed, they were the most efficient sleepers. Progressing from the most balanced through to the most inactive clusters, the time spent in prolonged sitting behaviours progressively increased. With the most balanced cluster spending an average 35% (SD:13%) of their sitting time in prolonged sitting activities compared to the most inactive cluster spending on average 61% (SD: 18%) of there sitting time in prolonged sitting activities. Indicating that not only were people in the most inactive cluster sitting for a greater percentage of time in their day, they also spent a greater percentage of sitting time in prolonged sitting activities. Conversely, and also progressing from the most balanced through to the most inactive clusters, the time spent walking when awake (i.e. movement balance) progressively decreased. With the most balanced cluster spending on average 34% (SD:6%) of their time when awake walking compared to the most inactive cluster spending on average 13% (SD:4%) of their awake time walking. Notably, all but the most inactive cluster had similar walking quality metrics, with the average walking quality in the more balanced, low sleeper and high sleeper clusters varying from 14\\u0026ndash;16%. Whereas, those in the most inactive cluster spent on average only 9% (SD:7%) of their walking time in higher cadence walking activities. Indicating that not only were people in the most inactive cluster spending a lower percentage of their day walking around, they also spent a smaller percentage of their walking time in higher cadence walking activities.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAcross-Cluster Comparison: Baseline Characteristics\\u003c/h2\\u003e \\u003cp\\u003eSee Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e for details of baseline characteristics across clusters and relative to the whole cohort, and Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e for details of percentage differences across clusters, relative to the whole cohort. Age was the only personal demographic characteristic that was significantly different across the clusters. Relative to the whole cohort, those in the most balanced and high sleeper clusters were younger (% Diff: -6.2 and \\u0026minus;\\u0026thinsp;6.9% younger), compared to those in low sleeper and most inactive clusters being older (% Diff: 8% and 10% older). None of the socio-economic characteristics were significantly different across the clusters, although relative to whole cohort, those in the most balanced cohort were more likely to have a marital spouse or partner, have a university education and have an annual household income greater than \\u003cspan\\u003e$\\u003c/span\\u003e80, with an opposite trend for these same socio-economic characteristics in the most inactive cluster [Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003e Percent Difference Cluster vs Whole Cohort: Selected Baseline Characteristics\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMost Inactive -Whole\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHigh Sleeper -Whole\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLow Sleeper -Whole\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMost Balanced -Whole\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ePersonal Demographics\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge (Years)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10.0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-6.9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-6.2%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSex (% Female)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-9.8%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.1%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI (kg / m2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.5%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-3.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-2.2%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eSocio-Economic Characteristics\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEmployed\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-33.8%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.1%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8.4%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSpouse / Partner\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-12.8%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-7.2%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13.2%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnnual Household Income \\u0026gt;\\u003cspan\\u003e$\\u003c/span\\u003e80K\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-26.7%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-6.8%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15.9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.1%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUniversity Degree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-13.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-9.1%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e18.1%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ePhysical / Mental Health\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDepression\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-5.7%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18.8%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-18.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.5%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFatigue\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-2.1%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.5%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-8.5%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.1%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePain\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20.0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-20.8%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-4.2%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eHabit Strength\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSitting \\u0026ndash; Home Leisure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.1%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-2.1%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-10.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSitting \\u0026ndash; Usual Occupation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.7%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.4%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-15.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWalking Outside\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-14.0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-7.0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eExternal Factors\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCovid Activity Restriction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-15.5%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16.8%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-6.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-4.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOPERAS Participant\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-13.9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-2.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI: Body Mass Index. OPERAS: On-demand Program to EmpoweR Active Self-management\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eInsert\\u003c/em\\u003e Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e \\u003cem\\u003ehere\\u003c/em\\u003e\\u003c/p\\u003e \\u003cp\\u003ePain and fatigue scores were also significantly different across clusters with these differences being most apparent when comparing the high and low sleeping clusters. Relative to the whole cohort, those in the low sleeping cluster reported lower levels of fatigue (% Diff: -8.5% less fatigue) and pain (% Diff: -20.8% less pain). Where as, those in the high sleeping cluster reported higher levels of, fatigue (% Diff: 8.5% more fatigue) and pain (% Diff: 20.0% more pain) relative to the whole cohort [Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eLeisure time sitting, usual occupational sitting and outside walking habit scores were also significantly different across the clusters. Notably, those in the most balanced cluster reported lower leisure time (% Diff: -10.6% weaker habit) and usual occupational sitting (% Diff: -15.6% weaker habit) habits and higher walking outside (% Diff: 9.3% stronger habit) habit scores relative to the whole cohort. Which is in contrast to those in the most inactive cluster reporting higher leisure time (% Diff: 10.6% stronger habit) and usual occupational sitting (% Diff: 6.7% stronger habit) habits and lower walking outside (% Diff: -14.0% weaker) habits relative to the whole cohort [Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e] Finally, there was no significant difference across clusters for the proportion of those in either the OPERAS or OPAM-IA study or for those assess during covid activity restrictions [Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and \\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBaseline Characteristics and Likelihood of Cluster Allocation\\u003c/h2\\u003e \\u003cp\\u003eSee Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e for details of the likelihood (Odd ratio) of different cluster allocation relative to a reference cluster for baseline characteristics remaining in the model following backwards, stepwise, multivariate regression analyses. Analysis highlighted that determinants of different patterns of 24-hour sleep-movement behaviours was multifactorial. Individuals were more likely to be allocated to the more balanced cluster, relative to the most inactive cluster, if they were a younger age (OR: 0.94, 95% CI: 0.90\\u0026ndash;0.98), had stronger walking outside habits (OR:1.44, 95% CI: 1.05\\u0026ndash;1.97) and weaker leisure time sitting habits (OR:0.62, 95% CI: 0.39\\u0026ndash;0.98 ). In addition, relative to the low sleep / high sit cluster, weaker usual occupational sitting habits was also associated with a greater likelihood of being in the more balanced cluster (OR: 0.61, 95% CI: 0.45\\u0026ndash;0.81). Stronger walking outside habits was also associated with a greater likelihood of being in the low sleep / high sit cluster, relative to the most inactive cluster (OR:1.36, 95% CI: 1.01\\u0026ndash;1.84). While, younger age (OR:0.94, 95% CI: 0.90\\u0026ndash;0.98) and greater fatigue (OR:1.59, 95% CI: 1.07\\u0026ndash;2.36) were associated with greater likelihood of being allocated to the high sleep / low walk cluster relative to the most inactive cluster. Multivariate regression analyses also found that sex, BMI, socio-economic factors, pain, depression, the study individuals volunteered for or the potential impacts of covid activity restrictions were not associated with cluster allocation.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eLikelihood of cluster allocation relative to reference cluster. Odd Ratio (95% Confidence Interval)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFactors Included in Model\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFactors Remaining in Model\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMost Balanced\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLow Sleep / High Sit\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eHigh sleep / Low Walk\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eHigh Sit / Low Walk (Reference)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003ePersonal Demographic Characteristics\\u003c/em\\u003e: Age (Years), Sex (F vs M), BMI (kg/m2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.94 (0.90\\u0026ndash;0.98)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.99 (0.95\\u0026ndash;1.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.94 (0.90\\u0026ndash;0.98)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e \\u003cem\\u003eSocio-Economic Factors\\u003c/em\\u003e: Spouse / Partner (yes / no), University Education (yes / no), Annual Household Income (+ / - \\u003cspan\\u003e$\\u003c/span\\u003e80K, unknown), Employed (yes / no)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003ePhysical / Mental Health Indicators\\u003c/em\\u003e: Pain (score), Fatigue (score), Depression - Mild to Severe (yes / no )\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFatigue\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.47 (0.98\\u0026ndash;2.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.97 (0.67\\u0026ndash;1.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.59 (1.07\\u0026ndash;2.36)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eSitting Habits\\u003c/em\\u003e: Home Leisure, Usual Occupational (score)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSitting - Home Leisure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.62 (0.39\\u0026ndash;0.98)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.65 (0.40\\u0026ndash;1.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.72 (0.45\\u0026ndash;1.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e*Sitting - Usual Occupational\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.61 (0.45\\u0026ndash;0.81)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.76 (0.57, 1.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.81 (0.58, 1.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSitting - Usual Occupational\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.74 (0.54\\u0026ndash;1.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.24 (0.88\\u0026ndash;1.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.94 (0.68\\u0026ndash;1.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eWalking Habits\\u003c/em\\u003e: Walking Outside \\u0026gt;\\u0026thinsp;10 minutes (score)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWalking - Outside\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.44 (1.05\\u0026ndash;1.97)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.36 (1.01\\u0026ndash;1.84)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.15 (0.85\\u0026ndash;1.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStudy Participation: OPERAS (yes / no)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCovid Activity Restriction: (yes / no)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c6\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBold\\u003c/b\\u003e\\u0026thinsp;=\\u0026thinsp;Statistically Significant * Most Balanced vs Low sleep / High Sit cluster as reference\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eInsert\\u003c/em\\u003e Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e \\u003cem\\u003ehere\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eThis study explored objectively measured sleep and awake behaviours in a cohort of adults living with RA, to identify unique patterns of 24-hour sleep and movement behaviours, and their association with common personal, socio-economic, physical, mental and existing sitting and walking habits. This study also presents how different patterns of 24-hour behaviours were associated with variations in sleep, sitting and walking quality, and the likelihood of meeting evidence-based benchmarks for steps and MVPA and 24-hour movement guidelines.\\u003c/p\\u003e \\u003cp\\u003eWe found that the cohort as a whole was getting acceptable sleep duration and quality. However, they spent more than three quarters of their awake time sitting, with almost half of their sitting time accumulated doing prolonged sitting activities. In addition, not only were people in the cohort spending less than a quarter of their day walking they were also only spending a small portion of their walking time in higher cadence walking activities. As such, other than getting acceptable sleep, the cohort as a whole did not meet benchmarks for walking, higher intensity activity or balanced 24-hour movement behaviours. These findings are consistent with previously published studies showing that on average adults with RA are generally more sedentary and less active than similar aged people, and commonly do not meet recommended daily steps or weekly MVPA recommendations [\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eWhen we used LCA to explore this further we found four distinctive patterns for how the adults living with RA were spending time sleeping and in awake movement activities throughout their day. One cluster, representing almost a quarter of the cohort, presented with more balanced 24-hour sleep-movement behaviour profile. Whereas, those in the other three clusters demonstrated progressively less balanced behaviour profiles; having either too little (\\u0026lt;\\u0026thinsp;7 hours), too much (\\u0026gt;\\u0026thinsp;8 hours), or enough sleep (7\\u0026ndash;8 hours) in respective combination with sitting too much (\\u0026gt;\\u0026thinsp;12 hours), walking to little (\\u0026lt;\\u0026thinsp;3 hours) or both when they were awake. We also found that having more balanced 24-hour sleep-movement behaviours was associated with better metrics for sleep, sitting and walking quality, and a greater likelihood of meeting evidence-based benchmarks for daily steps, MVPA and Canadian 24-hour movement guidelines. Together these findings suggests that many people living with RA can have a more balanced 24-hour sleep-movement lifestyle which may in turn be associated with better physical and mental health. Future research should explore how more balanced and various combinations of less balanced 24-hour sleep and awake movement behaviours may be associated with improved health outcomes in people living with RA, and other chronic health conditions.\\u003c/p\\u003e \\u003cp\\u003eOur findings also highlight that the importance of tailoring healthy lifestyle messages based on how individuals are actually spending their time sleeping, sitting and walking throughout their day. For some, the message would be \\u0026ldquo;you are doing well, keep it up\\u0026rdquo;. In others, who have lower levels of sleep and spend many hours sitting the focus would be about finding opportunities to replace sitting at home or during their usual occupational activities with more time in bed sleeping (\\u0026ldquo;sleep more / sit less\\u0026rdquo;). Whereas, those with too much sleep in combination with low levels of walking outside their home the focus would be more around finding opportunities to replace time in bed with outdoor walking activities (\\u0026ldquo;walk more / sleep less\\u0026rdquo;). Alternately, for others that have acceptable sleep but are inactive when awake, then the attention would be about finding ways to replace sitting with walking activities (\\u0026ldquo;walk more / sit less\\u0026rdquo;) [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e],\\u003c/p\\u003e \\u003cp\\u003eA distinctive finding is the association between existing sitting and walking habits and the likelihood of having a more or less balanced 24-hour sleep-activity profile while living with RA. Habitual behaviours are actions, or series of actions, that occur with limited conscious thought, often in response to contextual or environmental cues [\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]. The relationship between existing habits and future behaviours is complex, as habits can have both a moderating and mediating effect on future behaviours [\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e]. Pre-existing habits can be a predictor of future behaviours, independent of the intention to do a behaviour (i.e. habit as a mediator of future behaviour) [\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e]. As such, strong existing habitual behaviours are likely to increase the likelihood of future similar behaviours in similar contexts. However, strong existing habitual behaviours can also moderate the potential effect of the intention or desire of changing behaviours in similar contexts [\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e]. This speaks to the old adage that \\u0026ldquo;old habits are hard to break\\u0026rdquo;, which in turn may explain in part why strategies supporting someone to be more physically active do not necessarily change their existing sitting behaviours [\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e]. These finding support further explorations of the influence of strong or weak sitting or walking habits on activity-related health behavior change interventions in future investigations [\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eOur findings also highlight that differences in age, and physical or mental health may also be associated with having more or less balanced 24-hour sleep and movement profile. Notably age is not a modifiable factor, however, our findings do support the value of understanding not only the potential influence of existing habitual behaviours, but also the importance of managing co-existing factors like fatigue and pain when supporting a persons\\u0026rsquo; capacity, opportunity or motivation to modify their daily sleeping or movement behaviours [\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e]\\u003c/p\\u003e \\u003cp\\u003eThis study has limitations. Our findings would have limited generalizability to adults living with other chronic health conditions, or person\\u0026rsquo;s living with RA that are not inclined to volunteer for research studies or those with RA that did not meet the eligibility criteria for either study. This study is an exploratory, cross sectional study so any associations between differences in 24-hour sleep-activity behaviours and baseline characteristics cannot be defined in terms of the directionality of this relationship. It is possible that living with RA affects a person\\u0026rsquo;s 24-hour sleep and movement activity behaviours and / or that variations in 24-hour sleep-activity behaviours impact the physical, mental or other health outcomes in people living with RA [\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e]. Future studies should explore these potential relationships using longitudinal observational or experimental study designs. Another limitation is our use of research grade activity tracker to objectively measure 24-hour sleep and movement patterns, as these devices are expensive and not readily available. However, in clinical or usual life situations, where accurate minute by minute data for research purposes is not required, it is reasonable to consider using more affordable, accessible and acceptable consumer wearable activity trackers [\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e]. Consumer wearable devices can provide reasonable objective estimates of patterns of time spent in different activities over 24-hours to help guide and inform strategies to help a person to monitor and modify their 24-hour sleep and movement behaviours [\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e].\\u003c/p\\u003e\"},{\"header\":\"CONCLUSION\",\"content\":\"\\u003cp\\u003eFor adults living with RA, and potentially other chronic health conditions, it is important to understand the \\u0026lsquo;whole person\\u0026rsquo; and their \\u0026lsquo;whole day\\u0026rsquo; to help define who may benefit from support with modifying their 24-hour sleep-movement behaviours. Findings also highlight the importance of tailoring healthy lifestyle messages based on how individuals are actually spending their time sleeping, sitting and walking throughout their day. Ideally, the planning and implementation of supports to modify behaviours should be guided by objective measures of sleep and awake activities and adopt a shared-decision making approach to ensure that personal preferences and priorities are considered [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e]. In addition, supports should be are informed by an understanding of potentially modifiable personal or health related factors that could be acting as barriers or facilitators to behaviour change [\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e]. Including, exploring how habitually engrained existing sitting or walking behaviours may be, so that positive habits can be reinforced and strategies can be defined to help people identify and modify less positive habitual behaviours.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eRA: \\u0026nbsp; Rheumatoid Arthritis\\u003c/p\\u003e\\n\\u003cp\\u003eMVPA: Moderate to Vigorous Physical Activity\\u003c/p\\u003e\\n\\u003cp\\u003eOPAM-IA: Online Physical Activity Monitoring in Inflammatory Arthritis\\u003c/p\\u003e\\n\\u003cp\\u003eOPERAS: \\u0026nbsp;On-demand Program to EmpoweR Active Self-management\\u003c/p\\u003e\\n\\u003cp\\u003eMETs: Metabolic Equivalents\\u003c/p\\u003e\\n\\u003cp\\u003eSF-MPQ: Short Form-McGill Pain Questionnaire\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eFSS: Fatigue Severity Scale\\u003c/p\\u003e\\n\\u003cp\\u003ePHQ-9: Patient Health Questionnaire-9\\u003c/p\\u003e\\n\\u003cp\\u003eSRHI: Self Reported Habit Index\\u003c/p\\u003e\\n\\u003cp\\u003eLCA: Latent Class Analysis\\u003c/p\\u003e\\n\\u003cp\\u003eAIC / BIC: Akaike\\u0026rsquo;s and Bayesian Information Criterion\\u003c/p\\u003e\\n\\u003cp\\u003eSD: Standard Deviation\\u003c/p\\u003e\\n\\u003cp\\u003e% Diff: Percent Difference\\u003c/p\\u003e\\n\\u003cp\\u003eOR: Odds Ratio\\u003c/p\\u003e\\n\\u003cp\\u003e95% CI: 95% Confidence Intervals\\u003cstrong\\u003e\\u003cbr\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBoth studies were carried out in compliance with the Helsinki declaration for conducting research with humans and received ethical approval from the University of British Columbia, Vancouver, Canada (OPAM-IA study: H15-01843, OPERAS study: H17-03424). Participants provided written informed consent which including permission to use their data for research purposes.\\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNone of the authors have any direct or indirect financial or non-financial competing interests to declare.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was supported by two Arthritis Society Canada Strategic Operating Grants (Funding Reference Numbers: SOG-16-391; SOG-14-110). The funder did not have any input in the design or conduct of this study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eLF conceived the study idea. LCL is the lead author and person holding data stewardship for the baseline data collected from the two clinical trials from which these secondary data analyses were conducted. All authors (LF, HX, NL, LCL) were involved in the design of the study, the analyses plan and interpretation of the results. NL conducted the analyses. All authors were involved in manuscript preparation and editing. All authors read and approved the final version of the manuscript, prior to submission.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors would like to thank Jacob McIvor, MSc for his invaluable assistance with the Sensewear activity monitor data processing. \\u0026nbsp; We would also like to acknowledge the dedication and support from the research staff and trainees from Arthritis Research Canada who supported the OPAM-IA and OPERAS clinical trials. We are also grateful for the partnership with our patient/consumer collaborators, whose contributions were integral to the planning, conduct and dissemination of the OPAM-IA and OPERAS clinical trials. \\u003cstrong\\u003e\\u003cbr\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eRadu AF, Bungau SG. Management of rheumatoid arthritis: an overview. Cells. 2021;10(11):2857.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBlack RJ, Cross M, Haile LM, Culbreth GT, Steinmetz JD, Hagins H, Kopec JA, Brooks PM, Woolf AD, Ong KL, Kopansky-Giles DR. Global, regional, and national burden of rheumatoid arthritis, 1990\\u0026ndash;2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. Lancet Rheumatol. 2023;5(10):e594\\u0026ndash;610.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAlmutairi K, Nossent J, Preen D, Keen H, Inderjeeth C. The global prevalence of rheumatoid arthritis: a meta-analysis based on a systematic review. Rheumatol Int. 2021;41(5):863\\u0026ndash;77.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAllen A, Carville S, McKenna F. Diagnosis and management of rheumatoid arthritis in adults: summary of updated NICE guidance. 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BMC Musculoskelet Disord. 2017;18(1):1\\u0026ndash;2.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBj\\u0026ouml;rk M, Dragioti E, Alexandersson H, Esbensen BA, Bostr\\u0026ouml;m C, Friden C, Hjalmarsson S, H\\u0026ouml;rnberg K, Kjeken I, Regardt M, Sundelin G. Inflammatory Arthritis and the Effect of Physical Activity on Quality of Life and Self-Reported Function: A Systematic Review and Meta‐Analysis. Arthritis Care Res. 2022;74(1):31\\u0026ndash;43.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKhoja SS, Almeida GJ, Chester Wasko M, Terhorst L, Piva SR. Association of light-intensity physical activity with lower cardiovascular disease risk burden in rheumatoid arthritis. Arthritis Care Res. 2016;68(4):424\\u0026ndash;31.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKu PW, Hamer M, Liao Y, Hsueh MC, Chen LJ. Device-measured light‐intensity physical activity and mortality: A meta‐analysis. 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Kinesiology. 2014;46(1):135\\u0026ndash;46.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFalck RS, Davis JC, Khan KM, Handy TC, Liu-Ambrose T. A wrinkle in measuring time use for cognitive health: how should we measure physical activity, sedentary behaviour and sleep? Am J Lifestyle Med. 2023;17(2):258\\u0026ndash;75.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSteultjens M, Bell K, Hendry G. The challenges of measuring physical activity and sedentary behaviour in people with rheumatoid arthritis. Rheumatol Adv Pract. 2023;7(1):rkac101.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFeehan LM, Lu N, Xie H, Li LC. Twenty-Four Hour Activity and Sleep Profiles for Adults Living with Arthritis: Habits Matter. 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Physical activity automaticity, intention, and trait self-control as predictors of physical activity behavior\\u0026ndash;a dual-process perspective. Psychol Health Med. 2022;27(5):1021\\u0026ndash;34.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFeil K, Allion S, Weyland S, Jekauc D. A systematic review examining the relationship between habit and physical activity behavior in longitudinal studies. Front Psychol. 2021;12:626750.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChastin S, Gardiner PA, Harvey JA, Leask CF, Jerez-Roig J, Rosenberg D, Ashe MC, Helbostad JL, Skelton DA. Interventions for reducing sedentary behaviour in community-dwelling older adults. Cochrane Database of Systematic Reviews. 2021(6).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eThomas R, Berry A, Swales C, Cramp F. Strategies to enhance physical activity in people with Rheumatoid Arthritis: A Delphi survey. Musculoskelet Care. 2023 Mar 8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFenton SA, O\\u0026rsquo;Brien CM, Kitas GD, Duda JL, van Veldhuijzen JJ, Metsios GS. The behavioural epidemiology of sedentary behaviour in inflammatory arthritis: where are we, and where do we need to go? Rheumatol Adv Pract. 2023;7(1):rkac097.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFuller D, Colwell E, Low J, Orychock K, Tobin MA, Simango B, Buote R, Van Heerden D, Luan H, Cullen K, Slade L. Reliability and validity of commercially available wearable devices for measuring steps, energy expenditure, and heart rate: systematic review. JMIR mHealth and uHealth. 2020;8(9):e18694.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGermini F, Noronha N, Borg Debono V, Abraham Philip B, Pete D, Navarro T, Keepanasseril A, Parpia S, de Wit K, Iorio A. Accuracy and acceptability of wrist-wearable activity-tracking devices: systematic review of the literature. J Med Internet Res. 2022;24(1):e30791.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOcagli H, Agarinis R, Azzolina D, Zabotti A, Treppo E, Francavilla A, Bartolotta P, Todino F, Binutti M, Gregori D, Quartuccio L. Physical activity assessment with wearable devices in rheumatic diseases: a systematic review and meta-analysis. Rheumatology. 2023;62(3):1031\\u0026ndash;46.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBremander A, Malm K, Andersson ML, BARFOT Study Group. Physical activity in established rheumatoid arthritis and variables associated with maintenance of physical activity over a seven-year period\\u0026ndash;a longitudinal observational study. BMC Rheumatol. 2020;4:1\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDavergne T, Moe RH, Fautrel B, Gossec L. Development and initial validation of a questionnaire to assess facilitators and barriers to physical activity for patients with rheumatoid arthritis, axial spondyloarthritis and/or psoriatic arthritis. Rheumatol Int. 2020;40:2085\\u0026ndash;95.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFenton SA, Duda JL, van Zanten JJ, Metsios GS, Kitas GD. Theory-informed interventions to promote physical activity and reduce sedentary behaviour in rheumatoid arthritis: a critical review of the literature. Mediterranean J Rheumatol. 2020;31(1):19.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDavergne T, Tekaya R, Sellam J, Tournadre A, Mitrovic S, Ruyssen-Witrand A, Hudry C, Dadoun S, Avouac J, Fautrel B, Gossec L. Influence of perceived barriers and facilitators for physical activity on physical activity levels in patients with rheumatoid arthritis or spondyloarthritis: a cross-sectional study of 150 patients. BMC Musculoskelet Disord. 2021;22(1):1\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"journal-of-activity-sedentary-and-sleep-behaviors\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"jassb\",\"sideBox\":\"Learn more about [Journal of Activity, Sedentary and Sleep Behaviors](https://jassb.biomedcentral.com/)\",\"snPcode\":\"44167\",\"submissionUrl\":\"https://submission.nature.com/new-submission/44167/3\",\"title\":\"Journal of Activity, Sedentary and Sleep Behaviors\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Rheumatoid Arthritis, 24-hour sleep, movement, sedentary behavior, Latent Class Analysis\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3861599/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3861599/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eRheumatoid Arthritis (RA) is an auto-immune systemic inflammatory disease, affecting more than 17\\u0026nbsp;million people globally. People with RA commonly have other chronic health conditions, have a higher risk for premature mortality, often experience chronic fatigue, pain and disrupted sleep and are less physically active and more sedentary than healthy counterparts. What remains unclear is how people with RA may balance their time sleeping and participating in non-ambulatory or walking activities over 24-hours. Nor is it known how different 24-hour sleep-movement patterns may be associated with common determinants of health in people with RA.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eWe conducted a cross-sectional exploration of objectively measured 24-hour walking, non-ambulatory, and sleep activities in 203 adults with RA. We used Latent Class Analysis to identify 24-hour sleep-movement profiles and examined how different profiles were associated with sleep, sitting and walking quality and meeting published guidelines. We conducted multinomial logistic regression to identify factors associated with likelihood of belonging to individual profiles.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eWe identified 4 clusters, including one cluster (26%) with more balanced 24-hour sleep, sitting and walking behaviours. The other three clusters demonstrated progressively less balanced profiles; having either too little (\\u0026lt;\\u0026thinsp;7 hrs), too much (\\u0026gt;\\u0026thinsp;8 hrs), or enough sleep (7\\u0026ndash;8 hrs) in respective combination with sitting too much (\\u0026gt;\\u0026thinsp;12 hrs), walking to little (\\u0026lt;\\u0026thinsp;3 hrs) or both when awake. Age, existing sitting and walking habits and fatigue were associated with the likelihood of belonging to different profiles. More balanced 24-hour behaviour was associated with better metrics for sleep, sitting and walking quality and greater likelihood for meeting benchmarks for daily steps, weekly MVPA and Canadian 24-hour movement guidelines.\\u003c/p\\u003e\\u003ch2\\u003eDiscussion\\u003c/h2\\u003e \\u003cp\\u003eFor adults living with RA, and potentially other chronic health conditions, it is important to understand the \\u0026lsquo;whole person\\u0026rsquo; and their \\u0026lsquo;whole day\\u0026rsquo; to define who may benefit from support to modify 24-hour sleep-movement behaviours and for tailoring healthy lifestyle messages for which behaviours to modify. Supports should be are informed by an understanding of personal or health related factors that could be acting as barriers or facilitators to behaviour change including exploring how habitually engrained existing sitting or walking behaviours may be.\\u003c/p\\u003e\\u003ch2\\u003eTrial Registrations\\u003c/h2\\u003e \\u003cp\\u003eClinicalTrials.gov ID NCT02554474 (2015-09-16) and ClinicalTrials.gov ID NCT03404245 (2018-01-11)\\u003c/p\\u003e\",\"manuscriptTitle\":\"Twenty four-hour sleep, movement and sedentary activity profiles in adults living with Rheumatoid Arthritis: A cross-sectional latent class analysis\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-01-17 21:13:42\",\"doi\":\"10.21203/rs.3.rs-3861599/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-03-08T06:13:02+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-03-08T00:09:39+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-02-05T11:58:05+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"33a1af97-bcaf-4966-90b3-fe17b6e1aa47\",\"date\":\"2024-01-24T10:45:55+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-01-24T09:38:23+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-01-24T06:05:22+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-01-16T06:59:32+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Journal of Activity, Sedentary and Sleep Behaviors\",\"date\":\"2024-01-14T00:29:23+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"journal-of-activity-sedentary-and-sleep-behaviors\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"jassb\",\"sideBox\":\"Learn more about [Journal of Activity, Sedentary and Sleep Behaviors](https://jassb.biomedcentral.com/)\",\"snPcode\":\"44167\",\"submissionUrl\":\"https://submission.nature.com/new-submission/44167/3\",\"title\":\"Journal of Activity, Sedentary and Sleep Behaviors\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"6c8b83fb-6c97-4a01-a559-d773a48e5431\",\"owner\":[],\"postedDate\":\"January 17th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-03-27T07:46:13+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-01-17 21:13:42\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3861599\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3861599\",\"identity\":\"rs-3861599\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}